Measurement and Analysis of an Online Content Voting Network: A Case Study of Digg

Abstract: Emergence of online content voting networks allows users to share and rate content including social news, photos and videos. The basic idea behind online content voting networks is that aggregate user activities (e.g., submitting and rating content) makes high-quality content thrive through the unprecedented scale, high dynamics and divergent quality of user generated content (UGC). To better understand the nature and impact of online content voting networks, we have analyzed Digg, a popular online social news aggregator and rating website. Based on a large amount of data collected, we provide an in-depth study of Digg. In particular, we study structural properties of Digg social network, impact of social network properties on user digging activities and vice versa, distribution of user diggs, content promotion, and information filtering. We also provide insight into design of content promotion algorithms and recommendation-assisted content discovery. Overall, we believe that the results presented in this paper are crucial in understanding online content rating networks.