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

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.

References in corpus (1)

• Modeling Events with Cascades of Poisson Processes

Cited by in corpus (5)

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• Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit
• Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes
• Social Media and COVID-19: Can Social Distancing be Quantified without Measuring Human Movements?
• Unsupervised Text Mining of COVID-19 Records