Publication Type
Journal Article
Version
publishedVersion
Publication Date
10-2017
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.
Discipline
Models and Methods | Political Science | Social Media
Research Areas
Political Science
Publication
Statistics, Politics and Policy
Volume
8
Issue
1
First Page
85
Last Page
104
ISSN
2194-6299
Identifier
10.1515/spp-2017-0006
Publisher
De Gruyter
Citation
AMADOR DIAZ LOPEZ, Julio C., COLLIGNON-DELMAR, Sofia, BENOIT, Kenneth, & MATSUO, Akitaka.(2017). Predicting the Brexit Vote by tracking and classifying public opinion using Twitter data. Statistics, Politics and Policy, 8(1), 85-104.
Available at: https://ink.library.smu.edu.sg/soss_research/3991
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1515/spp-2017-0006