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

arXiv: https://arxiv.org/abs/2004.11692 · HTML: https://mlsanity.com/paper/2004.11692/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [A Holistic Framework for Analyzing the COVID-19 Vaccine Debate](https://mlsanity.com/paper/2205.01817/) (2022)
- [Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion](https://mlsanity.com/paper/2405.17957/) (2024)
- [ANTM: An Aligned Neural Topic Model for Exploring Evolving Topics](https://mlsanity.com/paper/2302.01501/) (2023)
- [An Analysis of COVID-19 Knowledge Graph Construction and Applications](https://mlsanity.com/paper/2110.04932/) (2021)
- [COVID-19 Tweets Analysis through Transformer Language Models](https://mlsanity.com/paper/2103.00199/) (2021)
- [Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes](https://mlsanity.com/paper/2301.06021/) (2023)
- [Unsupervised Text Mining of COVID-19 Records](https://mlsanity.com/paper/2110.07357/) (2021)

## Abstract (arXiv)

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.