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Votes on Twitter: Assessing Candidate Preferences and Topics of Discussion During the 2016 U.S. Presidential Election

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  • Anjie Fang
  • Philip Habel
  • Iadh Ounis
  • Craig MacDonald

Abstract

Social media offers scholars new and innovative ways of understanding public opinion, including citizens’ prospective votes in elections and referenda. We classify social media users’ preferences over the two U.S. presidential candidates in the 2016 election using Twitter data and explore the topics of conversation among proClinton and proTrump supporters. We take advantage of hashtags that signaled users’ vote preferences to train our machine learning model which employs a novel classifier—a Topic-Based Naive Bayes model—that we demonstrate improves on existing classifiers. Our findings demonstrate that we are able to classify users with a high degree of accuracy and precision. We further explore the similarities and divergences among what proClinton and proTrump users discussed on Twitter.

Suggested Citation

  • Anjie Fang & Philip Habel & Iadh Ounis & Craig MacDonald, 2019. "Votes on Twitter: Assessing Candidate Preferences and Topics of Discussion During the 2016 U.S. Presidential Election," SAGE Open, , vol. 9(1), pages 21582440187, March.
    • Handle: RePEc:sae:sagope:v:9:y:2019:i:1:p:2158244018791653
    DOI: 10.1177/2158244018791653
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    References listed on IDEAS

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    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," NBER Working Papers 23089, National Bureau of Economic Research, Inc.
    3. Bond, Robert & Messing, Solomon, 2015. "Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook," American Political Science Review, Cambridge University Press, vol. 109(1), pages 62-78, February.
    4. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 211-236, Spring.
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    Cited by:

    1. Kris Hartley & Minh Khuong Vu, 2020. "Fighting fake news in the COVID-19 era: policy insights from an equilibrium model," Policy Sciences, Springer;Society of Policy Sciences, vol. 53(4), pages 735-758, December.
    2. Emiliano del Gobbo & Sara Fontanella & Annalina Sarra & Lara Fontanella, 2021. "Emerging Topics in Brexit Debate on Twitter Around the Deadlines," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 669-688, August.

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