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

机译：我们如何变得多大？特朗普粉丝和克林顿粉丝的多式联数分类

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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：美国政治的极化已被广泛记录和分析数十年，在2016年总统选举中，现象变得更加明显，特朗普和克林顿描绘了两个彻底不同的美国图片。通过这种差距极化和2016年总统竞选期间的Twitter广泛利用，在本文中，我们在社交媒体中测量极化的第一步，我们试图通过分析'日常推文，配置文件图像来预测行为之后的个人推特并发布图片。因此，我们将极化视为分类问题，并且可以区分Twitter上的特朗普粉丝和克林顿粉丝的研究，这反过来是一般的极化度量。我们将LSTM应用于处理Tweet功能，并使用VGG神经网络提取视觉功能。集成这两套功能促进了整体性能。我们能够达到69％的准确性，表明文献中记录的高度的极化也开始在社交媒体中表现出来。

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