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

机译：我们变得多么两极化？特朗普追随者和克林顿追随者的多模式分类

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摘要

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.
机译：数十年来，美国政治中的两极分化已得到大量文献记录和分析，在2016年总统大选期间，这种现象变得更加明显，特朗普和克林顿描绘了美国的两幅截然不同的图画。受2016年总统大选期间这种巨大的两极分化和Twitter广泛使用的启发，本文我们迈出了衡量社交媒体两极分化的第一步，我们试图通过分析人们的日常推文，个人资料图片来预测个人的Twitter行为并张贴图片。因此，我们将两极分化视为分类问题，并研究在Twitter上可以将特朗普的追随者和克林顿的追随者区分开的程度，这反过来又是两极分化的度量标准。我们将LSTM应用于处理推特特征，并使用VGG神经网络提取视觉特征。集成这两组功能可提高整体性能。我们能够达到69％的准确性，这表明文献中记录的高度极化也已开始在社交媒体中体现出来。

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