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

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