  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.
