@article{Amador:2017:10.1515/spp-2017-0006,
author = {Amador, Diaz Lopez JC and Collignon-Delmar, S and Benoit, K and Matsuo, A},
doi = {10.1515/spp-2017-0006},
journal = {Statistics, Politics and Policy},
title = {Predicting the Brexit Vote by Tracking and Classifying Public Opinion Using Twitter Data},
url = {http://dx.doi.org/10.1515/spp-2017-0006},
volume = {8},
year = {2017}
}

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TY  - JOUR
AB  - 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.
AU  - Amador,Diaz Lopez JC
AU  - Collignon-Delmar,S
AU  - Benoit,K
AU  - Matsuo,A
DO  - 10.1515/spp-2017-0006
PY  - 2017///
SN  - 2151-7509
TI  - Predicting the Brexit Vote by Tracking and Classifying Public Opinion Using Twitter Data
T2  - Statistics, Politics and Policy
UR  - http://dx.doi.org/10.1515/spp-2017-0006
UR  - http://hdl.handle.net/10044/1/53122
VL  - 8
ER  - 

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