2024/05/18 by Stephen Scarano, Scarano, Stephen, Vijayalakshmi Vasudevan +11 · 1 voice
Computer Science · Physics and Astronomy · Social Sciences · #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Physical sciences #Hate Speech and Cyberbullying Detection #Media Influence and Politics #Physics and Society (physics.soc-ph) #Social Media and Politics #Social and Information Networks (cs.SI) #cs.CY #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.2405.11146
openalex publication_date 2024/05/18 · arxiv published 2024/05/18 · arxiv updated 2024/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Social media platforms allow users to create polls to gather public opinion on diverse topics. However, we know little about what such polls are used for and how reliable they are, especially in significant contexts like elections. Focusing on the 2020 presidential elections in the U.S., this study shows that outcomes of election polls on Twitter deviate from election results despite their prevalence. Leveraging demographic inference and statistical analysis, we find that Twitter polls are disproportionately authored by older males and exhibit a large bias towards candidate Donald Trump relative to representative mainstream polls. We investigate potential sources of biased outcomes from the point of view of inauthentic, automated, and counter-normative behavior. Using social media experiments and interviews with poll authors, we identify inconsistencies between public vote counts and those privately visible to poll authors, with the gap potentially attributable to purchased votes. We also find that Twitter accounts participating in election polls are more likely to be bots, and election poll outcomes tend to be more biased, before the election day than after. Finally, we identify instances of polls spreading voter fraud conspiracy theories and estimate that a couple thousand of such polls were posted in 2020. The study discusses the implications of biased election polls in the context of transparency and accountability of social media platforms.