Analyzing Support for U.S. Presidential Candidates in Twitter Polls

Authors

  • Stephen Scarano University of Massachusetts, Amherst
  • Vijayalakshmi Vasudevan University of Massachusetts, Amherst
  • Mattia Samory Sapienza University of Rome, Italy
  • JungHwan Yang University of Illinois Urbana-Champaign
  • Przemyslaw Grabowicz University of Massachusetts, Amherst

DOI:

https://doi.org/10.51685/jqd.2024.icwsm.4

Keywords:

public opinion, social media, opinion polls

Abstract

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.

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Published

2024-05-29

How to Cite

Scarano, S., Vasudevan, V., Samory, M., Yang, J., & Grabowicz, P. (2024). Analyzing Support for U.S. Presidential Candidates in Twitter Polls. Journal of Quantitative Description: Digital Media , 4. https://doi.org/10.51685/jqd.2024.icwsm.4

Issue

Section

ICWSM 2024 Special Issue