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Predicting the Brexit Vote by Tracking and Classifying Public Opinion Using Twitter Data

Author

Listed:
  • Amador Diaz Lopez Julio Cesar

    (Imperial College London, London SW7 2AZ, United Kingdom of Great Britain and Northern Ireland)

  • Collignon-Delmar Sofia

    (University College London, London, United Kingdom of Great Britain and Northern Ireland)

  • Benoit Kenneth
  • Matsuo Akitaka

    (London School of Economics and Political Science – Methodology, London, United Kingdom of Great Britain and Northern Ireland)

Abstract

We use 23M Tweets related to the EU referendum in the UK to predict the Brexit vote. In particular, we use user-generated labels known as hashtags to build training sets related to the Leave/Remain campaign. Next, we train SVMs in order to classify Tweets. Finally, we compare our results to Internet and telephone polls. This approach not only allows to reduce the time of hand-coding data to create a training set, but also achieves high level of correlations with Internet polls. Our results suggest that Twitter data may be a suitable substitute for Internet polls and may be a useful complement for telephone polls. We also discuss the reach and limitations of this method.

Suggested Citation

  • Amador Diaz Lopez Julio Cesar & Collignon-Delmar Sofia & Benoit Kenneth & Matsuo Akitaka, 2017. "Predicting the Brexit Vote by Tracking and Classifying Public Opinion Using Twitter Data," Statistics, Politics and Policy, De Gruyter, vol. 8(1), pages 85-104, October.
    • Handle: RePEc:bpj:statpp:v:8:y:2017:i:1:p:85-104:n:7
    DOI: 10.1515/spp-2017-0006
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    References listed on IDEAS

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    1. Settle, Jaime E. & Bond, Robert M. & Coviello, Lorenzo & Fariss, Christopher J. & Fowler, James H. & Jones, Jason J., 2016. "From Posting to Voting: The Effects of Political Competition on Online Political Engagement," Political Science Research and Methods, Cambridge University Press, vol. 4(2), pages 361-378, May.
    2. Huberty, Mark, 2015. "Can we vote with our tweet? On the perennial difficulty of election forecasting with social media," International Journal of Forecasting, Elsevier, vol. 31(3), pages 992-1007.
    3. Nicholas Beauchamp, 2017. "Predicting and Interpolating State‐Level Polls Using Twitter Textual Data," American Journal of Political Science, John Wiley & Sons, vol. 61(2), pages 490-503, April.
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    Cited by:

    1. Sequeira, Sandra & Nardotto, Mattia, 2021. "Identity, Media and Consumer Behavior," CEPR Discussion Papers 15765, C.E.P.R. Discussion Papers.
    2. Simon Rudkin & Lucy Barros & Paweł Dłotko & Wanling Qiu, 2024. "An economic topology of the Brexit vote," Regional Studies, Taylor & Francis Journals, vol. 58(3), pages 601-618, March.

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