2000 character limit reached
Public versus Less-Public News Engagement on Facebook: Patterns Across Bias and Reliability
Published 19 May 2023 in cs.SI, cs.CY, and cs.HC | (2305.11943v3)
Abstract: The rapid growth of social media as a news platform has raised significant concerns about the influence and societal impact of biased and unreliable news on these platforms. While much research has explored user engagement with news on platforms like Facebook, most studies have focused on publicly shared posts. This focus leaves an important question unanswered: how representative is the public sphere of Facebook's entire ecosystem? Specifically, how much of the interactions occur in less-public spaces, and do public engagement patterns for different news classes (e.g., reliable vs. unreliable) generalize to the broader Facebook ecosystem? This paper presents the first comprehensive comparison of interaction patterns between Facebook's more public sphere (referred to as public in paper) and the less public sphere (referred to as private). For the analysis, we first collect two complementary datasets: (1) aggregated interaction data for all Facebook posts (public + private) for 19,050 manually labeled news articles (225.3M user interactions), and (2) a subset containing only interactions with public posts (70.4M interactions). Then, through discussions and iterative feedback from the CrowdTangle team, we develop a robust method for fair comparison between these datasets. Our analysis reveals that only 31% of news interactions occur in the public sphere, with significant variations across news classes. Engagement patterns in less-public spaces often differ, with users, for example, engaging more deeply in private contexts. These findings highlight the need to examine both public and less-public engagement to fully understand news dissemination on Facebook. The observed differences hold important implications on content moderation, platform governance, and policymaking, contributing to healthier online discourse.
- Walker, M., Matsa, K.E.: News Consumption Across Social Media in 2021. Pew Research Center (2021). https://www.pewresearch.org/journalism/2021/09/20/news-consumption-across-social-media-in-2021 Thompson et al. [2020] Thompson, N., Wang, X., Daya, P.: Determinants of news sharing behavior on social media. Journal of Computer Information Systems 60(6), 593–601 (2020) https://doi.org/10.1080/08874417.2019.1566803 Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. CHI ’20, pp. 1–14. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3313831.3376784 Nah and Yamamoto [2018] Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Thompson, N., Wang, X., Daya, P.: Determinants of news sharing behavior on social media. Journal of Computer Information Systems 60(6), 593–601 (2020) https://doi.org/10.1080/08874417.2019.1566803 Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. CHI ’20, pp. 1–14. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3313831.3376784 Nah and Yamamoto [2018] Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. CHI ’20, pp. 1–14. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3313831.3376784 Nah and Yamamoto [2018] Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Thompson, N., Wang, X., Daya, P.: Determinants of news sharing behavior on social media. Journal of Computer Information Systems 60(6), 593–601 (2020) https://doi.org/10.1080/08874417.2019.1566803 Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. CHI ’20, pp. 1–14. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3313831.3376784 Nah and Yamamoto [2018] Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. CHI ’20, pp. 1–14. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3313831.3376784 Nah and Yamamoto [2018] Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. CHI ’20, pp. 1–14. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3313831.3376784 Nah and Yamamoto [2018] Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.