Modeling Trust and Influence on Blogosphere using Link Polarity

There is a growing interest in exploring the role of social networks for understanding how communities and individuals spread influence. In a densely connected world where much of our communication happens online, social media and networks have a great potential in influencing our thoughts and actions. The key contribution of our work is generation of a fully-connected polar social network graph from the sparsely connected social network graph in the context of blogs, where the vertex represents a blogger and the weight of an edge in the polar network represents the bias/trust/distrust between its connecting vertices (the source and destination bloggers). Our approach uses the link structure of blog graph to associate sentiments with the links connecting two blogs. (By link we mean the url that blogger a uses in his blog post to refer to post from blogger b). We term this sentiment as link polarity and the sign and magnitude of this value is based on the sentiment of text surrounding the link. We then use trust propagation models to spread this sentiment from a subset of connected blogs to other blogs to generate the fully connected polar blog graph. Our simple heuristics for analysis of text surrounding links and generation of missing polar links (links with positive or negative sentiment) using trust propagation is highly applicable for domains having weak link structure. This work has numerous applications such as finding ?like minded? blogs, detecting influential bloggers, locating bloggers with specific biases about a predefined set of topics etc. Our experimental validation on determining ?like minded? blogs on the political blogosphere demonstrates the potential of using polar links for more generic problems such as detecting trustworthy nodes in web graphs.