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

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

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...

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Bibliographic Details
Main Authors: AMADOR DIAZ LOPEZ, Julio C., COLLIGNON-DELMAR, Sofia, BENOIT, Kenneth, MATSUO, Akitaka
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
Subjects:
Models and Methods
Political Science
Social Media
Online Access:https://ink.library.smu.edu.sg/soss_research/3991
https://ink.library.smu.edu.sg/context/soss_research/article/5249/viewcontent/BrexitVote_2017_pv.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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