Abstract
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.
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Notes
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Individuals who follow both candidates constitute a surprisingly small portion of the entire follower population. For a more detailed analysis that includes also Bernie Sanders, please see [38].
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The datasets and codes are available at https://sites.google.com/site/wangyurochester/papers.
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Acknowledgement
We acknowledge support from the Department of Political Science at the University of Rochester, from the New York State through the Goergen Institute for Data Science, and from our corporate sponsors. We also thank the four anonymous reviewers for their insightful comments and suggestions.
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Wang, Y., Feng, Y., Hong, Z., Berger, R., Luo, J. (2017). How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10539. Springer, Cham. https://doi.org/10.1007/978-3-319-67217-5_27
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DOI: https://doi.org/10.1007/978-3-319-67217-5_27
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