Measurement and analysis of an online content voting network

Abstract

In online content voting networks, aggregate user activities (e.g., submitting and rating content) make 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. We study structural properties of Digg social network, revealing some strikingly distinct properties such as low link symmetry and the power-law distribution of node outdegree with truncated tails. We explore impact of the social network on user digging activities, and investigate the issues of content promotion, content filtering, vote spam and content censorship, which are inherent to content rating networks. 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.