Why do People Share Misinformation during the COVID-19 Pandemic?

Abstract: The World Health Organization have emphasised that misinformation - spreading rapidly through social media - poses a serious threat to the COVID-19 response. Drawing from theories of health perception and cognitive load, we develop and test a research model hypothesizing why people share unverified COVID-19 information through social media. Our findings suggest a person's trust in online information and perceived information overload are strong predictors of unverified information sharing. Furthermore, these factors, along with a person's perceived COVID-19 severity and vulnerability influence cyberchondria. Females were significantly more likely to suffer from cyberchondria, however, males were more likely to share news without fact checking their source. Our findings suggest that to mitigate the spread of COVID-19 misinformation and cyberchondria, measures should be taken to enhance a healthy skepticism of health news while simultaneously guarding against information overload.

• The paper empirically examines factors driving COVID-19 misinformation sharing among Facebook users in Bangladesh using models like HBM, PMT, and CLT.
• Key findings show that trust in online information and perceived information overload are significant predictors of sharing misinformation, while perceived health threats are not.
• The study suggests interventions focusing on reducing information overload and enhancing digital literacy could help mitigate the spread of online health misinformation during crises.

Analysis of Misinformation Spread During the COVID-19 Pandemic

The research paper by Samuli Laato, A.K.M. Najmul Islam, Muhammad Nazrul Islam, and Eoin Whelan presents an empirical examination of the drivers behind the sharing of misinformation during the COVID-19 pandemic. Given the critical challenges presented by misinformation in public health contexts, this study engages deeply with the confluence of technological trust and information overload, alongside health perception constructs, to understand cyberchondria and the dissemination of unverified information on social media.

Core Findings and Methodological Approach

The research leverages the Health Belief Model (HBM), Protection-Motivation Theory (PMT), and Cognitive Load Theory (CLT) to construct a comprehensive model scrutinizing the factors influencing misinformation sharing on social media during the COVID-19 crisis. Using data from 294 Facebook users in Bangladesh, analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM), the researchers identified two primary predictors for the sharing of unverified information: trust in online information and perceived information overload.

The study's decision to focus on Bangladesh provides a pertinent setting due to the significant penetration of social media in the region during the pandemic, as highlighted by Internet World Stats data. The analysis showed that those who trusted social media content were more likely to share misinformation, a finding consistent with previous literature, while perceived health threats were not directly linked to misinformation dissemination. This suggests that social trust mechanisms and cognitive overwhelm trumped perceived morphological severity or vulnerability in driving such behavior.

Furthermore, the study provided insights into the gender dynamics at play, revealing that females reported higher levels of cyberchondria, while males demonstrated a greater propensity to share unverified information. These gendered insights emphasize the nuanced psychological and sociocultural dimensions that influence health-related behaviors on social platforms.

Implications for Theory and Practice

The theoretical contribution of this study stems from its integration of misinformation sharing and cyberchondria, particularly within a pandemic context, marking a novel intersection in existing research that previously treated these phenomena separately. This fusion of cognitive load constructs with health behavior theories provides a multidimensional perspective on individual decision-making processes concerning online health information—a perspective that is critical for developing interventions.

Practically, the work suggests that interventions aimed at reducing information overload and fostering source skepticism could alleviate misinformation spread and associated cyberchondria. The identification of online information trust as a pivotal factor implies that digital literacy programs and platform-based fact-checking initiatives might prove effective.

Prospects for Future Research

Despite its robust methodological framework, the study confines itself to a cross-sectional design, capturing a specific temporal window of the pandemic's progression, which may not reflect evolving behaviors over time. Future longitudinal studies could expand on these findings by incorporating political, technological, and societal dimensions that influence misinformation and its reception at the macro level.

This study lays the groundwork for deeper inquiries into the role cognitive load plays in information processing during crises, inviting further scholarly exploration into creating supportive digital ecosystems that safeguard public health information integrity in future global health events.