Author: Sha, Hao; Hasan, Mohammad Al; Mohler, George; Brantingham, P. Jeffrey
Title: Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives Cord-id: r6pqv89x Document date: 2020_4_19
ID: r6pqv89x
Snippet: 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 causal
Document: 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.
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