Cookies?
Library Header Image
LSE Research Online LSE Library Services

Predicting the Brexit vote by tracking and classifying public opinion using Twitter data

Amador Diaz Lopez, Julio Cesar and Collignon-Delmar, Sofia and Benoit, Kenneth 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

[img] Text - Accepted Version
Restricted to Repository staff only

Download (345kB) | Request a copy

Identification Number: 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

Item Type: Article
Official URL: https://www.degruyter.com/searchwithinjournal?sour...
Additional Information: © 2017 Walter de Gruyter GmbH
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
Sets: Departments > Methodology
Date Deposited: 09 Feb 2018 12:26
Last Modified: 09 Feb 2018 12:36
URI: http://eprints.lse.ac.uk/id/eprint/86731

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics