Modeling community structure and topics in dynamic text networks

TL;DR

This paper introduces a Bayesian approach combining dynamic network and topic modeling to uncover community structures in political blogs, showing that community membership correlates with blog topics.

Contribution

The paper presents a novel Bayesian method that integrates topic discovery with network modeling, enhancing understanding of community structures in dynamic text networks.

Findings

Identified complex community structures in political blogs.

Community membership strongly depends on blog topics.

Method effectively links topics to network communities.

Abstract

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.