Contextual Linking Behavior of Bloggers: Leveraging text-mining to enable topic-based analysis [pdf]
To be published in Social Network Analysis and Mining.
Read the official published paper online at Springer.

The last decade has seen an explosion in blogging and the blogosphere is continuing to grow, having a large global reach and many vibrant communities. Researchers have been pouring over blog data with the goal of finding communities, tracking what people are saying, finding influencers, and using many social network analytic tools to analyze the underlying social networks embedded within the blogosphere. One of the key technical problems with analyzing large social networks such as those embedded in the blogosphere is that there are many links between individuals and we often do not know the context or meaning of those links. This is problematic because it makes it difficult if not impossible to tease out the true communities, their behavior, how information flows, and who the central players are (if any). This paper seeks to further our understanding of how to analyze large blog networks and what they can tell us. We analyze 1.13M blogs posted by 185K bloggers over a period of 3 weeks. These bloggers span private blog sites through large blog-sites such as LiveJournal and Blogger. We show that we can, in fact, tag links in meaningful ways by leveraging topic-detection over the blogs themselves. We use these topics to contextually tag links coming from a particular blog post. This enrichment enables us to create smaller topic-specific graphs which we can analyze in some depth. We show that these topic-specific graphs not only have a different topology from the general blog graph but also enable us to find central bloggers which were otherwise hard to find. We further show that a temporal analysis identifies behaviors in terms of how components form as well as how bloggers continue to link after components form. These behaviors come to light when doing an analysis on the topic-specific graphs but are hidden or not easily discernable when analyzing the general blog graph.