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Statistics, Politics and Policy

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2151-7509
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Predicting the Brexit Vote by Tracking and Classifying Public Opinion Using Twitter Data

Julio Cesar Amador Diaz Lopez / Sofia Collignon-Delmar
  • University College London, London, United Kingdom of Great Britain and Northern Ireland
  • University of Strathclyde, Glasgow, United Kingdom of Great Britain and Northern Ireland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Kenneth Benoit
  • London School of Economics and Political Science – Methodology, London, United Kingdom of Great Britain and Northern Ireland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Akitaka Matsuo
  • London School of Economics and Political Science – Methodology, London, United Kingdom of Great Britain and Northern Ireland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-09-29 | DOI: https://doi.org/10.1515/spp-2017-0006

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.

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About the article

Published Online: 2017-09-29

Published in Print: 2017-10-26


Citation Information: Statistics, Politics and Policy, Volume 8, Issue 1, Pages 85–104, ISSN (Online) 2151-7509, ISSN (Print) 2194-6299, DOI: https://doi.org/10.1515/spp-2017-0006.

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