Votes on Twitter
Assessing Candidate Preferences and Topics of Discussion During the 2016 U.S. Presidential Election
Bibliographic Data
| ID | 7540307 |
|---|---|
| Authors | Anjie Fang (University of Glasgow, corresponding author), Philip Habel (University of South Alabama), Iadh Ounis (0000-0003-4701-3223, University of Glasgow), Craig Macdonald (0000-0003-3143-279X, University of Glasgow) |
| Year | 2019 |
| Volume | 9 |
| Issue | 1 |
| Publication date | 2019-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | SAGE Open (JOURNAL) |
| Journal identifiers | ISSN: 2158-2440 • E-ISSN: 2158-2440 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/2158244018791653 |
| OpenAlex | W2904414842 |
| Language | EN |
| Citations received | 4 |
| References cited | 23 |
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
Classifier (UML · Conversation · Naive Bayes classifier · Political science · Politics · Presidential election · Presidential system · Public opinion · Sentiment analysis · Social media · Voting · World Wide Web · Computer Science · Misinformation and Its Impacts · Opinion Dynamics and Social Influence · Psychology · Social Media and Politics · Artificial Intelligence
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| Unique citing works | 4 |
|---|---|
| Citations per year | 0,67 |
| Citation span | 2020 - 2022 (3) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 4 |