Modeling community structure and topics in dynamic text networks
This work addresses the challenge of analyzing dynamic text networks for researchers in social network analysis and computational social science, though it appears incremental as it combines existing areas of dynamic network and topic modeling.
The paper tackled the problem of jointly modeling community structure and topics in dynamic text networks by developing a Bayesian method that integrates topic discovery with latent network modeling, applied to 467 top political blogs from 2012, resulting in the identification of complex community structures strongly linked to bloggers' topic interests.
The last decade has seen great progress in both dynamic network modeling and topic modeling. This paper draws upon both areas to create a Bayesian method that allows topic discovery to inform the latent network model and the network structure to facilitate topic identification. We apply this method to the 467 top political blogs of 2012. Our results find complex community structure within this set of blogs, where community membership depends strongly upon the set of topics in which the blogger is interested.