Motivation

1. Trust on Blogosphere using Link Polarity Anubhav Kale, Akshay Java, Pranam Kolari, Dr Anupam Joshi, Dr Tim Finin Link Polarity Computation Experiments Motivation ● Test Dataset from Buzzmetrics [3] contains 12 M post-post links and reference dataset from Adamic et al [4] contains 300 blogs labeled as left and right leaning. ● Goal is to classify blogs in Buzzmetrics 1 . Can you track the buzz for iPod in blogs ? 2. Can you find the blogs that are iPod fans and iPod haters ? 3 . In general, how can you target the right set of individuals - “like-minded blogs” for advertising ? Trust Propagation 1. Guha et al [1] model based on applying atomic propagations iteratively. 2. Mi+1 = Mi * Ci – Perform till convergence M = Belief Matrix; Ci = Atomic Propagation Ci = M + MT*M + MT + M*MT Problem Statement Convert a sparsely connected “non-polar” blog graph into a densely connected “polar” graph with sentiments across each edge and use the “polar” graph to model trust. Approach Direct Transpose • Sentiment detection to determine “polarity” of blog-blog links • Trust Propagation to create polar links between blogs having no explicit links • Label blogs as left or right leaning based on their polarity from influential blogs Co citation Coupling [1] Guha et al - http://citeseer.ist.psu.edu/guha04propagation.html [2] http://www.pacificviews.org/weblog/archives/001989.html [3] Buzzmetrics - http://www.nielsenbuzzmetrics.com/ [4] Adamic et al - http://portal.acm.org/citation.cfm?id=1134271.1134277