1. Trust on Blogosphere using Link Polarity Anubhav Kale, Akshay Java, Pranam Kolari, Dr Anupam Joshi, Dr Tim Finin
Motivation
Link Polarity Computation
Experiments
● 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 -
[2]
[3] Buzzmetrics -
[4] Adamic et al -