Amador Diaz Lopez, Julio Cesar, Collignon-Delmar, Sofia, Benoit, Kenneth 
ORCID: 0000-0002-0797-564X and Matsuo, Akitaka
(2017)
Predicting the Brexit vote by tracking and classifying public opinion using Twitter data.
Statistics, Politics and Policy, 8 (1).
ISSN 2194-6299
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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
| Item Type: | Article |
|---|---|
| Official URL: | https://www.degruyter.com/searchwithinjournal?sour... |
| Additional Information: | © 2017 Walter de Gruyter GmbH |
| Divisions: | Methodology |
| Subjects: | H Social Sciences > HE Transportation and Communications J Political Science > JN Political institutions (Europe) J Political Science > JN Political institutions (Europe) > JN101 Great Britain J Political Science > JZ International relations |
| Date Deposited: | 09 Feb 2018 12:26 |
| Last Modified: | 15 Nov 2024 03:51 |
| URI: | http://eprints.lse.ac.uk/id/eprint/86731 |
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