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. (2019) Votes on Twitter: assessing candidate preferences and topics of discussion during the 2016 U.S. presidential election. SAGE Open, 9(1), (doi: 10.1177/2158244018791653)

[img]
Preview
Text
164781.pdf - Published Version
Available under License Creative Commons Attribution.

1MB

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:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Professor Craig and Fang, Mr Anjie and Habel, Dr Philip and Ounis, Professor Iadh
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
Published Online:27 March 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in SAGE Open 9(1)
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record