Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives
| dc.contributor.author | Sha, Hao | |
| dc.contributor.author | Al Hasan, Mohammad | |
| dc.contributor.author | Mohler, George | |
| dc.contributor.author | Brantingham, P. | |
| dc.contributor.department | Computer and Information Science, School of Science | en_US |
| dc.date.accessioned | 2020-07-27T18:31:14Z | |
| dc.date.available | 2020-07-27T18:31:14Z | |
| dc.date.issued | 2020-04-19 | |
| dc.description.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. | en_US |
| dc.identifier.citation | Sha, H., Hasan, M., Mohler, G., & Brantingham, P. (2020). Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1805/23392 | |
| dc.language.iso | en_US | en_US |
| dc.source | ArXiv | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | Social Media | en_US |
| dc.subject | Government | en_US |
| dc.subject | United States | en_US |
| dc.subject | Dynamic Topic Model | en_US |
| dc.title | Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives | en_US |
| dc.type | Preprint | en_US |