Modeling Community Structure and Topics in Dynamic Text Networks

Teague R. Henry (), David Banks, Derek Owens-Oas and Christine Chai
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Teague R. Henry: University of North Carolina
David Banks: Duke University
Derek Owens-Oas: Duke University
Christine Chai: Duke University

Journal of Classification, 2019, vol. 36, issue 2, No 9, 322-349

Abstract: 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.

Keywords: Networks; Natural language processing; Topic modeling; Political blogs; Community detection (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00357-018-9289-3

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