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Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives

A combination of federal and state-level decision making has shaped the response to COVID-19 in the United States. In this paper we analyze the Twitter narratives around this decision making by applying a dynamic topic model to COVID-19 related tweets by U.S. Governors and Presidential cabinet members. We use a network Hawkes binomial topic model to track evolving sub-topics around risk, testing and treatment. We also construct influence networks amongst government officials using Granger causality inferred from the network Hawkes process.

preprint2020arXivOpen access
Hao ShaMohammad Al HasanGeorge MohlerP. Jeffrey Brantingham
Social and Information Networksphysics.soc-ph
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Open access4 authors2 topics
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Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Hao ShaMohammad Al HasanGeorge MohlerP. Jeffrey Brantingham

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