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

TL;DR

This paper analyzes how U.S. governors and cabinet members' Twitter discussions on COVID-19 evolved over time, revealing shifts in topics like risk and testing, and mapping influence networks among officials.

Contribution

It introduces a dynamic topic model combined with influence network analysis to study political communication during COVID-19.

Findings

Identified evolving sub-topics related to COVID-19 risk, testing, and treatment.

Mapped influence networks among government officials using Granger causality.

Revealed temporal shifts in narratives and influence patterns.

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.