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.
References
Barberá, P.: Birds of the same feather tweet together: Bayesian ideal point estimation using twitter data. Polit. Anal. 23(1), 76–91 (2015)
Burger, J.D., Henderson, J., Kim, G., Zarrella, G.: Discriminating gender on twitter. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (2011)
Campbell, J.E.: Polarized: Making Sense of a Divided America. Princeton University Press, Princeton (2016)
Chen, T., Chen, Y., Luo, J.: A selfie is worth a thousand words: mining personal patterns behind user selfie-posting behaviours. In: Proceedings of the 26th International World Wide Web Conference (2017)
Doherty, C.: 7 things to know about polarization in America. Pew Research Center, Washington, D.C. (2014)
Druckman, J.N., Peterson, E., Slothuus, R.: How elite partisan polarization affects public opinion formation. Am. Polit. Sci. Rev. 107(1), 57–79 (2013)
Fiorina, M.P., Abrams, S.J.: Political polarization in the American public. Annu. Rev. Polit. Sci. 11, 563–588 (2008)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press, Cambridge (2016)
Hare, C., Poole, K.T.: The polarization of contemporary American politics. Polity 46(3), 411–429 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Political Polarization in the American Public
Irsoy, O., Cardie, C.: Opinion mining with deep recurrent neural networks. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 720–728 (2014)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751 (2014)
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
Levi, G., Hassner, T.: Age and gender classification using deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 34–42 (2015)
Lin, Y., Lei, H., Wu, J., Li, X.: An empirical study on sentiment classification of Chinese review using word embedding. In: Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation (2015)
Liu, P., Joty, S., Meng, H.: Fine-grained opinion mining with recurrent neural networks and word embeddings. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1433–1443 (2015)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: 2015 IEEE International Conference on Computer Vision, pp. 3730–3738 (2015)
McCarty, N., Poole, K.T., Rosenthal, H.: Does gerrymandering cause polarization? Am. J. Polit. Sci. 53(3), 666–680 (2009)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27, 415–444 (2001)
Pascanu, R., Gulcehre, C., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Proceedings of the Second International Conference on Learning Representations (ICLR 2014) (2014)
Paul, R., Hawkins, S.H., Hall, L.O., Goldgof, D.B., Gillies, R.J.: Combining deep neural network and traditional image features to improve survival prediction accuracy for lung cancer patients from diagnostic CT. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (2016)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Preotiuc-Pietro, D., Volkova, S., Lampos, V., Bachrach, Y., Aletras, N.: Studying user income through language, behaviour and affect in social media. PLoS One 10(9), e0138717 (2015)
Rao, A., Spasojevic, N.: Actionable and political text classification using word embeddings and LSTM. arXiv:1607.02501 (2016)
Sanders, B.: Our Revolution: A Future to Believe. Thomas Dunne Books, New York (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations 2015 (2015)
Stahl, L.: President-elect trump speaks to a divided country on 60 minutes. CBS (2016)
Wang, Y., Feng, Y., Luo, J.: Gender politics in the 2016 U.S. presidential election: a computer vision approach. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 35–45 (2017)
Wang, Y., Feng, Y., Luo, J., Zhang, X.: Pricing the woman card: gender politics between Hillary Clinton and Donald Trump. In: IEEE International Conference on Big Data (2016)
Wang, Y., Feng, Y., Zhang, X., Luo, J.: Voting with feet: who are leaving Hillary Clinton and Donald Trump? In: Proceedings of the IEEE Symposium on Multimedia (2016)
Wang, Y., Feng, Y., Zhang, X., Luo, J.: Inferring follower preferences in the 2016 US presidential primaries with sparse learning. In: Lee, D., Lin, Y.R., Osgood, N., Thomson, R. (eds.) SBP-BRiMS 2017. LNCS, vol. 10354, pp. 3–13. Springer, Cham (2017). doi:10.1007/978-3-319-60240-0_1
Wang, Y., Li, Y., Luo, J.: Deciphering the 2016 U.S. presidential campaign in the twitter sphere: a comparison of the Trumpists and Clintonists. In: Tenth International AAAI Conference on Web and Social Media (2016)
Wang, Y., Liao, H., Feng, Y., Xue, X., Luo, J.: Do they all look the same? Deciphering Chinese, Japanese and Koreans by fine-grained deep learning. arXiv:1610.01854v2 (2016)
Wang, Y., Luo, J., Niemi, R., Li, Y., Hu, T.: Catching fire via ‘likes’: inferring topic preferences of Trump followers on twitter. In: Tenth International AAAI Conference on Web and Social Media (2016)
Wang, Y., Luo, J., Zhang, X.: When follow is just one click away: understanding twitter follow behavior in the 2016 U.S. presidential election. In: International Conference on Social Informatics (2017)
Wang, Y., Yuan, J., Luo, J.: To love or to loathe: how is the world reacting to China’s rise? In: Workshop Proceedings International Conference on Data Mining 2015 (2015)
Ounis, I., Yang, X., Macdonald, C.: Using word embeddings in twitter election classification. arXiv:1606.07006 (2016)
You, Q., Luo, J., Jin, H., Yang, J.: Robust image sentiment analysis using progressively trained and domain transferred deep networks. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (2015)
Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization (2015). https://arxiv.org/pdf/1409.2329v5.pdf
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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