A powerful insight into trust and bias in social media.

1. Trust, Influence and Bias in Social Media Anupam Joshi Joint work with Tim Finin and several students Ebiquity Group, UMBC [email_address] http://ebiquity.umbc.edu/

13. Approximating Communities ICWSM ‘08 Additional evaluations using Variation of Information score

15. Social Media Graphs Links Between Nodes Links Between Nodes and Tags Simultaneous Cuts

16. A community in the real world is identified in a graph as a set of nodes that have more links within the set than outside it and share similar tags. Communities in Social Media

17. Nodes Nodes Nodes Tags Tags Nodes Tags Tags Fiedler Vector Polarity β = 0 Entirely ignore link information β = 1 Equal importance to blog-blog and blog-tag, β >> 1 NCut WebKDD ‘08 SimCUT: Clustering Tags and Graphs

18. SimCUT: Clustering Tags and Graphs β = 0 Entirely ignore link information β = 1 Equal importance to blog-blog and blog-tag, β >> 1 NCut Clustering Only Links Clustering Links + Tags WebKDD ‘08

20. Clustering Tags and Graphs Clustering Only Links Clustering Links + Tags

21. Varying Scaling Parameter β Accuracy = 36% Accuracy = 62% Higher accuracy by adding ‘tag’ information Simple Kmeans ~23% Content only, binary Content only ~52% (Getoor et al. 2004) β >> 1 β=1 β=0 Accuracy = 39% Only Graph Only Tags Graphs & Tags

23. Influence in Communities http://instapundit.com http://michellemalkin.com/ http://dailykos.com http://crooksandliars.com http://volokh.com http://rightwingnews.com Communities detected using “Fast algorithm for detecting community structure in networks”, M.E. J. Newman

25. Link Polarity & Sentiment

36. MSM Classification Results

38. Identifying Bias using KL Divergence

39. Conclusion

42. http://ebiquity.umbc.edu