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Modeling Community Structure and Topics in Dynamic Text Networks

Author

Listed:
  • Teague R. Henry

    (University of North Carolina)

  • David Banks

    (Duke University)

  • Derek Owens-Oas

    (Duke University)

  • Christine Chai

    (Duke University)

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 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.

Suggested Citation

  • Teague R. Henry & David Banks & Derek Owens-Oas & Christine Chai, 2019. "Modeling Community Structure and Topics in Dynamic Text Networks," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 322-349, July.
    • Handle: RePEc:spr:jclass:v:36:y:2019:i:2:d:10.1007_s00357-018-9289-3
    DOI: 10.1007/s00357-018-9289-3
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    References listed on IDEAS

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    Cited by:

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    2. Anton Oleinik, 2024. "A Bayesian index of association: comparison with other measures and performance," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 277-305, February.

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