Votes on Twitter: assessing candidate preferences and topics of discussion during the 2016 U.S. presidential election

Fang, A., Habel, P., Ounis, I. and Macdonald, C. (2018) Votes on Twitter: assessing candidate preferences and topics of discussion during the 2016 U.S. presidential election. SAGE Open, (Accepted for Publication)

Fang, A., Habel, P., Ounis, I. and Macdonald, C. (2018) Votes on Twitter: assessing candidate preferences and topics of discussion during the 2016 U.S. presidential election. SAGE Open, (Accepted for Publication)

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Abstract

Social media offers scholars new and innovative ways of understanding public opinion, including citizens' prospective votes in elections and referenda. We classify social media users' preferences over the two U.S. presidential candidates in the 2016 election using Twitter data and explore the topics of conversation among proClinton and proTrump supporters. We take advantage of hashtags that signaled users' vote preferences to train our machine learning model which employs a novel classifier-a Topic Based Naive Bayes model-that we demonstrate improves on existing classifiers. Our findings demonstrate that we are able to classify users with a high degree of accuracy and precision. We further explore the similarities and divergences among what proClinton and proTrump users discussed on Twitter.

Item Type:Articles
Keywords:Twitter, election, classification, topic modelling.
Status:Accepted for Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Dr Craig and Ounis, Professor Iadh and Fang, Mr Anjie and Habel, Dr Philip
Authors: Fang, A., Habel, P., Ounis, I., and Macdonald, C.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:SAGE Open
Publisher:SAGE
ISSN:2158-2440
ISSN (Online):2158-2440

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