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

Amador Diaz Lopez Julio Cesar (), Collignon-Delmar Sofia, Benoit Kenneth and Matsuo Akitaka
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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: London School of Economics and Political Science – Methodology, London, United Kingdom of Great Britain and Northern Ireland
Matsuo Akitaka: London School of Economics and Political Science – Methodology, London, United Kingdom of Great Britain and Northern Ireland

Statistics, Politics and Policy, 2017, vol. 8, issue 1, 85-104

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.

Date: 2017
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DOI: 10.1515/spp-2017-0006

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