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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.

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