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Modeling Trust and Influence in the Blogosphere using Link Polarity

Modeling Trust and Influence in the Blogosphere using Link Polarity. Anubhav Kale et al. (ICWSM 2007) University of Maryland. Presented by Sai Moturu. Introduction. The theme is similar to Nitin’s work but the approach is different

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Modeling Trust and Influence in the Blogosphere using Link Polarity

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  1. Modeling Trust and Influencein the Blogosphereusing Link Polarity Anubhav Kale et al. (ICWSM 2007) University of Maryland Presented by Sai Moturu

  2. Introduction • The theme is similar to Nitin’s work but the approach is different • Use links in the blog graph to associate blog-blog link sentiment for a domain • This is called link polarity – has a sign and magnitude based on the sentiment

  3. Sentiment detection • Analyze text around the link in the source blog • A window of ‘X’ characters before and after • Use a corpus of positive/negative words • Calculating link polarity:

  4. Trust Propagation • Blog graphs are not always densely connected • A sentiment spread mechanism is needed • Idea from Guha et al., 2004 • Distrust is propagated too (not just one-step)

  5. Data • Buzzmetrics data set • Over 1.3 million blog posts • Provides link structure between posts • Domain: Political Blogs • Reasonably high link density • Minimal number of off-the-topic posts • Two sets: Republican & Democratic bloggers • 300 blogs

  6. Classification • A positive score for blog B from the top three democratic blogs (pre-defined) indicates that blog B is Democratic and a negative score indicates that it is Republican

  7. Results

  8. Conclusions • Novel approach for classifying blogs (observe that the focus shifts from modeling trust and influence) • Preliminary results are promising. Future work will improve upon this

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