Abstract

Effective response to pandemics requires coordinated adoption of mitigation measures, like masking and quarantines, to curb a virus’s spread. However, as the COVID-19 pandemic demonstrated, political divisions can hinder consensus on the appropriate response. To better understand these divisions, our study examines a vast collection of COVID-19-related tweets. We focus on five contentious issues: coronavirus origins, lockdowns, masking, education, and vaccines. We describe a weakly supervised method to identify issue-relevant tweets and employ state-of-the-art computational methods to analyze moral language and infer political ideology. We explore how partisanship and moral language shape conversations about these issues. Our findings reveal ideological differences in issue salience and moral language used by different groups. We find that conservatives use more negatively-valenced moral …

Metadata

publication

2023 IEEE International Conference on Big Data (BigData), 413-422, 2023

year

2023

publication date

2023/12/15

authors

Ashwin Rao, Siyi Guo, Sze Yuh Nina Wang, Fred Morstatter, Kristina Lerman

link

https://ieeexplore.ieee.org/abstract/document/10386084/

resource_link

https://arxiv.org/pdf/2305.18533

conference

2023 IEEE International Conference on Big Data (BigData)

pages

413-422

publisher

IEEE