document: Measurement and analysis of an online content voting network : a case study of Digg 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 .
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