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Polarization in American politics has been extensively documented andanalyzed for decades, and the phenomenon became all the more apparent duringthe 2016 presidential election, where Trump and Clinton depicted two radicallydifferent pictures of America. Inspired by this gaping polarization and theextensive utilization of Twitter during the 2016 presidential campaign, in thispaper we take the first step in measuring polarization in social media and weattempt to predict individuals' Twitter following behavior through analyzingones' everyday tweets, profile images and posted pictures. As such, we treatpolarization as a classification problem and study to what extent Trumpfollowers and Clinton followers on Twitter can be distinguished, which in turnserves as a metric of polarization in general. We apply LSTM to processingtweet features and we extract visual features using the VGG neural network.Integrating these two sets of features boosts the overall performance. We areable to achieve an accuracy of 69%, suggesting that the high degree ofpolarization recorded in the literature has started to manifest itself insocial media as well.

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