How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers

By Yu Wang, Yang Feng, Zhe Hong, Ryan Berger, Jiebo Luo. 2017

28 citations recorded by the source corpus.

Polarization in American politics has been extensively documented and analyzed for decades, and the phenomenon became all the more apparent during the 2016 presidential election, where Trump and Clinton depicted two radically different pictures of America. Inspired by this gaping polarization and the extensive utilization of Twitter during the 2016 presidential campaign, in this paper we take the first step in measuring polarization in social media and we attempt to predict individuals' Twitter following behavior through analyzing ones' everyday tweets, profile images and posted pictures. As such, we treat polarization as a classification problem and study to what extent Trump followers and Clinton followers on Twitter can be distinguished, which in turn serves as a metric of polarization in general. We apply LSTM to processing tweet features and we extract visual features using the VGG neural network. Integrating these two sets of features boosts the overall performance. We are able to achieve an accuracy of 69%, suggesting that the high degree of polarization recorded in the literature has started to manifest itself in social media as well.

Subfields: Artificial Intelligence, Communication, Sociology and Political Science, Multimodal Learning

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Citations in the corpus

FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age (2019, background): “Notable examples are demographic analyses of social media users using their photographs [9, 43, 64, 65, 62].”
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation (2021, background): “No-table examples are demographic analyses of social media users using their photographs [9, 45, 65, 66, 63].”
Understanding the Political Ideology of Legislators from Social Media Images (2019, methodology): “Wang et al. (2017) also uses social media data to characterize conservative and liberal voters, using Twitter followers of Trump and Clinton in 2016 U.S. Presidential Election (Wang, Feng, and Luo, 2017).”