AUTHOR=Dey Sreemanti , Ebanks Daniel , Hashash Sarah , Alvarez R. Michael 

TITLE=Detecting and measuring social media attacks on American election officials

JOURNAL=Frontiers in Political Science

VOLUME=Volume 7 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/political-science/articles/10.3389/fpos.2025.1488363

DOI=10.3389/fpos.2025.1488363

ISSN=2673-3145

ABSTRACT=The 2020 presidential election saw election officials experience physical and social media threats, harassment, and animosity. Although little research exists regarding animosity toward US election officials, observers noted a sharp increase in 2020 in animosity toward US election officials. The harassment of election officials hindered their work in administering a free and fair election and may have generated doubts about electoral integrity. Our study: (1) Proposes a unique measurement and modeling strategy applicable across many social media networks to study toxicity directed at officials, institutions, or groups; (2) Collects a novel dataset of social media conversations about election administration in the 2020 election; (3) Uses joint sentiment-topic modeling to identify toxicity from the reactions of the public and election officials, and uses dynamic vector autoregression models to determine the temporal structure of the toxic conversations directed at election officials; (4) Finds that the level of animosity toward election officials spikes immediately after the election, that hostile topics overall make up about a quarter of the discussion share during this period, increasing to about 60% following the election, and that hostile topics come from left- and right-wing partisans. Our article concludes by discussing how similar data collection and topic modeling approaches could be deployed in future elections to monitor trolling and harassment of election officials, and to mitigate similar threats to successful election administration globally.