Election Polls on Social Media: Prevalence, Biases, and Voter Fraud Beliefs
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908400919511040 |
|---|---|
| author | Scarano, Stephen Vasudevan, Vijayalakshmi Samory, Mattia Yang, Kai-Cheng Yang, JungHwan Grabowicz, Przemyslaw A. |
| author_facet | Scarano, Stephen Vasudevan, Vijayalakshmi Samory, Mattia Yang, Kai-Cheng Yang, JungHwan Grabowicz, Przemyslaw A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11146 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Election Polls on Social Media: Prevalence, Biases, and Voter Fraud Beliefs Scarano, Stephen Vasudevan, Vijayalakshmi Samory, Mattia Yang, Kai-Cheng Yang, JungHwan Grabowicz, Przemyslaw A. Social and Information Networks Computers and Society Physics and Society 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. |
| title | Election Polls on Social Media: Prevalence, Biases, and Voter Fraud Beliefs |
| topic | Social and Information Networks Computers and Society Physics and Society |
| url | https://arxiv.org/abs/2405.11146 |