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Gender Politics in the 2016 U.S. Presidential Election: A Computer Vision Approach

  • Yu WangEmail author
  • Yang Feng
  • Jiebo Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)

Abstract

Gender plays an important role in the 2016 U.S. presidential election, especially with Hillary Clinton becoming the first female presidential nominee and Donald Trump being frequently accused of sexism. In this paper, we introduce computer vision to the study of gender politics and present an image-driven method that can measure the effects of gender in an accurate and timely manner. We first collect all the profile images of the candidates’ Twitter followers. Then we train a convolutional neural network using images that contain gender labels. Lastly, we classify all the follower and unfollower images. Through a case study of the ‘woman card’ controversy, we demonstrate how gender is informing the 2016 presidential election. Our framework of analysis can be readily generalized to other case studies and elections.

Keywords

Gender politics Computer vision Hillary Clinton Donald Trump 

References

  1. 1.
    Brians, C.L.: Women for Women? Gender and Party Bias in Voting for Female Candidates, American Politics Research (2005)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Dolan, K.: Is There a “Gender Affinity Effect” in American Politics? Affect, and Candidate Sex in U.S. House Elections. Political Research Quarterly, Information (2008)Google Scholar
  4. 4.
    Farfade, S.S., Saberian, M., Li, L.-J.: Multi-view face detection using deep convolutional neural networks. In: ICMR (2015)Google Scholar
  5. 5.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  6. 6.
    Heckman, J.J.: Sample selection bias as a specification error. Econometrica 47(1), 153–161 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report, University of Massachusetts (2007)Google Scholar
  8. 8.
    Jia, S., Cristianini, N.: Learning to classify gender from four million images. Pattern Recogn. Lett. 58, 35–41 (2015)CrossRefGoogle Scholar
  9. 9.
    Jones, J.M.: Gender gap in 2012 vote is largest in gallup’s history (2012). http://www.gallup.com/poll/158588/gender-gap-2012-vote-largest-gallup-history.aspx
  10. 10.
    Ricanek, Jr., K., Tesafaye, T.: Morph: a longitudinal image database of normal adult age-progression. In: 7th International Conference on Automatic Face and Gesture Recognition (FGR 2006) (2006)Google Scholar
  11. 11.
    King, D.C., Matland, R.E.: Sex and the grand old party: an experimental investigation of the effect of candidate sex on support for a republican candidate. Am. Politics Res. 74(3), 633–640 (2003)Google Scholar
  12. 12.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)Google Scholar
  13. 13.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRefGoogle Scholar
  14. 14.
    MacWilliams, M.C.: Forecasting congressional elections using facebook data. PS: Polit. Sci. Politics 48(04), 579–583 (2015)Google Scholar
  15. 15.
    Mislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P., Rosenquist, J.N.: Understanding the demographics of twitter users. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (2011)Google Scholar
  16. 16.
    O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (2010)Google Scholar
  17. 17.
    Phillips, P.J., Wechslerb, H., Huangb, J., Raussa, P.J.: The FERET database and evaluation procedure for face-recognition algorithms. Image Vis. Comput. 16(5), 295–306 (1998)CrossRefGoogle Scholar
  18. 18.
    Saad, L.: Big gender gap distinguishes election 2000 (2000). http://www.gallup.com/poll/2884/big-gender-gap-distinguishes-election-2000.aspx
  19. 19.
    Stout, C.T., Kline, R.: Political Behavior (2010)Google Scholar
  20. 20.
    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)Google Scholar
  21. 21.
    Wang, Y., Luo, J., Niemi, R., Li, Y.: To follow or not to follow: analyzing the growth patterns of the Trumpists on Twitter. In: Workshop Proceedings of the 10th International AAAI Conference on Web and Social Media (2016)Google Scholar
  22. 22.
    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)Google Scholar
  23. 23.
    Wang, Y., Zhang, X., Luo, J.: When follow is just one click away: Understanding twitter follow behavior in the 2016 U.S. Presidential Election. arXiv:1702.00048 (2017)

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of RochesterRochesterUSA

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