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Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives
April 19, 2020 Β· Declared Dead Β· π ICWSM Workshops
"No code URL or promise found in abstract"
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Authors
Hao Sha, Mohammad Al Hasan, George Mohler, P. Jeffrey Brantingham
arXiv ID
2004.11692
Category
cs.SI: Social & Info Networks
Cross-listed
physics.soc-ph
Citations
65
Venue
ICWSM Workshops
Last Checked
5 months ago
Abstract
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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