Votes on Twitter: Assessing Candidate Preferences and Topics of Discussion During the 2016 U.S. Presidential Election

Anjie Fang, Philip Habel, Iadh Ounis and Craig MacDonald

SAGE Open, 2019, vol. 9, issue 1, 2158244018791653

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.

Keywords: computer science; user classification; topic modelling; twitter; political science; social science (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:9:y:2019:i:1:p:2158244018791653

DOI: 10.1177/2158244018791653

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