Predicting the Brexit Vote by Tracking and Classifying Public Opinion Using Twitter Data
File(s)
Accepted version
Author(s)
Amador Diaz Lopez, JC
Collignon-Delmar, S
Benoit, K
Matsuo, A
Type
Journal Article
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 Issued
2017-09-29
Date Acceptance
2017-09-05
Citation
Statistics, Politics and Policy, 2017, 8 (1)
ISSN
2151-7509
Publisher
De Gruyter
Journal / Book Title
Statistics, Politics and Policy
Volume
8
Issue
1
Copyright Statement
© 2017 Walter de Gruyter GmbH
Publication Status
Published