Modeling Community Structure and Topics in Dynamic Text Networks


The last decade has seen great progress in both dynamic network modeling and topic modeling. This paper draws upon both areas to create a bespoke Bayesian model applied to a dataset consisting of the top 467 US political blogs in 2012, their posts over the year, and their links to one another. Our model allows dynamic topic discovery to inform the latent network model and the network structure to facilitate topic identification. 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. We examine the time varying nature of the Sensational Crime topic, as well as the network properties of the Election News topic, as notable and easily interpretable empirical examples.

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  1. 1.

    Gingrich, Santorum, and Cain all refer to candidates in the 2012 Republican presidential primary.

  2. 2.

    George Zimmerman shot and killed Trayvon Martin in March of 2012.

  3. 3.

    Newt Gingrich gradually faded to political irrelevance after a failed presidential primary run.

  4. 4.

    Trayvon Martin was a young African American man shot by George Zimmerman, in what he claimed to be an act of self defense, while Martin was walking in Zimmerman’s neighborhood. The Aurora theater massacre was a mass shooting at a movie theater in Aurora, Colorado. The Sikh Temple shooting was a mass shooting at a Sikh temple in Wisconsin. The Sandy Hook massacre was a mass shooting at an elementary school in Connecticut.


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Correspondence to Teague R. Henry.

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Henry, T.R., Banks, D., Owens-Oas, D. et al. Modeling Community Structure and Topics in Dynamic Text Networks. J Classif 36, 322–349 (2019).

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  • Networks
  • Natural language processing
  • Topic modeling
  • Political blogs
  • Community detection