An official website of the United States government
Polls posted on social media can provide information about public opinion on a variety of issues from business decisions to support for presidential election candidates. However, it is largely unknown whether the information provided by social polls is useful or not. To enhance our understanding of social polls, we examine nearly two thousand Twitter polls gauging support for U.S. presidential candidates during the 2016 and 2020 election campaigns. First, we describe the prevalence of social polls. Second, we characterize social polls in terms of the engagement they elicit and the response options they present. Third, leveraging machine learning models, we infer and describe several characteristics, including demographics and political leanings, of the users who author and interact with social polls. Finally, we study the relationship between social poll results, their attributes, and the characteristics of users interacting with them. Our findings suggest how and to what extent polling on Twitter is biased in terms of content, authorship, and audience. The 2016 and 2020 polls were predominantly crafted by older males and manifested a pronounced bias favoring candidate Donald Trump, whereas traditional surveys favored Democratic candidates. We further identify and explore the potential reasons for such biases and discuss their repercussions.
more »
« less
This content will become publicly available on June 7, 2026
Election Polls on Social Media: Prevalence, Biases, and Voter Fraud Beliefs
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 male Republicans and exhibit a large bias towards candidate Donald Trump in comparison to 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 find that election polls tend to be more biased, contain more questionable votes, and attract more bots before the election day than after. We highlight and compare key factors contributing to biased poll outcomes. Finally, we identify instances of polls spreading voter fraud conspiracy theories and estimate that a couple of thousand 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.
more »
« less
- Award ID(s):
- 2432051
- PAR ID:
- 10613230
- Editor(s):
- Scarano, Stephen; Vasudevan, Vijayalakshmi; Samory, Mattia; Yang, Kai-Cheng; Yang, JungHwan; Grabowicz, Przemyslaw A
- Publisher / Repository:
- Proceedings of the Nineteenth International AAAI Conference on Web and Social Media
- Date Published:
- Volume:
- 19
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract With historic misses in the 2016 and 2020 US Presidential elections, interest in measuring polling errors has increased. The most common method for measuring directional errors and non-sampling excess variability during a postmortem for an election is by assessing the difference between the poll result and election result for polls conducted within a few days of the day of the election. Analysing such polling error data is notoriously difficult with typical models being extremely sensitive to the time between the poll and the election. We leverage hidden Markov models traditionally used for election forecasting to flexibly capture time-varying preferences and treat the election result as a peek at the typically hidden Markovian process. Our results are much less sensitive to the choice of time window, avoid conflating shifting preferences with polling error, and are more interpretable despite a highly flexible model. We demonstrate these results with data on polls from the 2004 through 2020 US Presidential elections and 1992 through 2020 US Senate elections, concluding that previously reported estimates of bias in Presidential elections were too extreme by 10%, estimated bias in Senatorial elections was too extreme by 25%, and excess variability estimates were also too large.more » « less
-
Abstract Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter’s news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.more » « less
-
Abstract Traditionally, election polls have asked for participants’ own voting intentions. In four elections, we previously found that we could improve predictions by asking participants how they thought their social circles would vote. A potential concern is that the social-circle question might predict results less well in elections with larger numbers of political options because it becomes harder to accurately track how social contacts plan to vote. However, we now find that the social-circle question performs better than the own-intention question in predicting two elections with many political parties: The Netherlands’ 2017 general election and the Swedish 2018 general election.more » « less
-
The increased social media usage in modern history instigates data collection from various users with different backgrounds. Mass media has been a rich source of information and might be utilized for countless purposes, from business and personal to political determination. Because more people tend to express their opinions through social media platforms, researchers are excited to collect data and use it as a free survey tool on what the public ponders about a particular issue. Because of the detrimental effect of news on social networks, many irresponsible users generate and promote fake news to influence public belief on a specific issue. The U.S. presidential election has been a significant and popular event, so both parties invest and extend their efforts to pursue and win the general election. Undoubtedly, spreading and promoting fake news through social media is one of the ways negligent individuals or groups sway societies toward their goals. This project examined the impact of removing fake tweets to predict the electoral outcomes during the 2020 general election. Eliminating mock tweets has improved the correctness of model prediction from 74.51 percent to 86.27 percent with the electoral outcomes of the election. Finally, we compared classification model performances with the highest model accuracy of 99.74634 percent, precision of 99.99881 percent, recall of 99.49430 percent, and an F1 score of 99.74592 percent. The study concludes that removing fake tweets improves the correctness of the model with the electoral outcomes of the U.S. election.more » « less
