Digg Data Trace
Dr. Yingwu Zhu
Project -- 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) 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. 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 ltering, 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. Relevant
Publications:
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