Understanding Discourse Acts: Political Campaign Messages Classification on Facebook and Twitter
To understand political campaign messages in depth, we developed automated classification models for classifying categories of political campaign Twitter and Facebook messages, such as calls-to-action and persuasive messages. We used 2014 U.S. governor’s campaign social media messages to develop models, then tested these models on a randomly selected 2016 U.S. presidential campaign social media dataset. Our classifiers reach .75 micro-averaged F value on training sets and .76 micro-averaged F value on test sets, suggesting that the models can be applied to classify English-language political campaign social media messages. Our study also suggests that features afforded by social media help improve classification performance in social media documents.
KeywordsAutomated classification Political campaign Social media Supervised learning Text mining
We thank Dr. Bei Yu’s helpful feedback on this paper. The project was supported by the Tow Center for Digital Journalism at Columbia University and the Center for Computational and Data Sciences at the School of Information Studies at Syracuse University.
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