When Follow is Just One Click Away: Understanding Twitter Follow Behavior in the 2016 U.S. Presidential Election

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10539)

Abstract

Motivated by the two paradoxical facts that the marginal cost of following one extra candidate is close to zero and that the majority of Twitter users choose to follow only one or two candidates, we study the Twitter follow behaviors observed in the 2016 U.S. presidential election. Specifically, we complete the following tasks: (1) analyze Twitter follow patterns of the presidential election on Twitter, (2) use negative binomial regression to study the effects of gender and occupation on the number of candidates that one follows, and (3) use multinomial logistic regression to investigate the effects of gender, occupation and celebrities on the choice of candidates to follow.

References

  1. 1.
    Alaimo, K.: Where Donald Trump got his real power. CNN (2016)Google Scholar
  2. 2.
    Barberá, P.: Birds of the same feather tweet together. Bayesian ideal point estimation using Twitter data. Polit. Anal. 23(1), 76–91 (2015)CrossRefGoogle Scholar
  3. 3.
    Brians, C.L.: Women for women? Gender and party bias in voting for female candidates. Am. Polit. Res. (2005)Google Scholar
  4. 4.
    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
  5. 5.
    Burt, R.S.: Decay functions. Soc. Netw. 22, 1–28 (2000)CrossRefGoogle Scholar
  6. 6.
    Campbell, J.E.: Polarized: Making Sense of a Divided America. Princeton University Press, Princeton (2016)CrossRefGoogle Scholar
  7. 7.
    Doherty, C.: 7 things to know about polarization in America. Pew Research Center (2014)Google Scholar
  8. 8.
    Dolan, K.: Is there a “gender affinity effect” in American politics? Information affect, and candidate sex in U.S. House elections. Polit. Res. Q. (2008)Google Scholar
  9. 9.
    Druckman, J.N., Peterson, E., Slothuus, R.: How elite partisan polarization affects public opinion formation. Am. Polit. Sci. Rev. 107(1), 57–79 (2013)CrossRefGoogle Scholar
  10. 10.
    Farfade, S.S., Saberian, M., Li, L.-J.: Multi-view face detection using deep convolutional neural networks. In: ICMR (2015)Google Scholar
  11. 11.
    Greene, W.: Functional forms for the negative binomial model for count data. Econ. Lett. 99, 585–590 (2008)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87–102. Springer, Cham (2016). doi:10.1007/978-3-319-46487-9_6 CrossRefGoogle Scholar
  13. 13.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Burlington (2011)MATHGoogle Scholar
  14. 14.
    Hare, C., Poole, K.T.: The polarization of contemporary american politics. Polity 46(3), 411–429 (2014)CrossRefGoogle Scholar
  15. 15.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  16. 16.
    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
  17. 17.
    Jia, S., Cristianini, N.: Learning to classify gender from four million images. Pattern Recogn. Lett. (2015)Google Scholar
  18. 18.
    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 (FGR06) (2006)Google Scholar
  19. 19.
    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. Polit. Res. (2003)Google Scholar
  20. 20.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)Google Scholar
  21. 21.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature (2015)Google Scholar
  22. 22.
    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)Google Scholar
  23. 23.
    Lockhart, K.: Watch: why social media is Donald Trump’s most powerful weapon. The Telegraph, September 2016Google Scholar
  24. 24.
    Maddala, G.S.: Limited-Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge (1983)CrossRefMATHGoogle Scholar
  25. 25.
    McCarty, N., Poole, K.T., Rosenthal, H.: Does gerrymandering cause polarization? Am. J. Polit. Sci. 53(3), 666–680 (2009)CrossRefGoogle Scholar
  26. 26.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. (2001)Google Scholar
  27. 27.
    Miller, C.C.: Why women did not unite to vote against Donald Trump. The New York Times, November 2016Google Scholar
  28. 28.
    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
  29. 29.
    Nilizadeh, S., Groggel, A., Lista, P., Das, S., Ahn, Y.-Y., Kapadia, A., Rojas, F.: Twitter’s glass ceiling: the effect of perceived gender on online visibility. In: Proceedings of the Tenth International AAAI Conference on Web and Social Media (2016)Google Scholar
  30. 30.
    Ottoni, R., Pesce, J.P., Casas, D.L., Franciscani Jr., G., Meira Jr., W., Kumaraguru, P., Almeida, V.: Ladies first: analyzing gender roles and behaviors in Pinterest. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (2013)Google Scholar
  31. 31.
    Phillips, P.J., Wechslerb, H., Huangb, J., Raussa, P.J.: The feret database and evaluation procedure for face-recognition algorithms. Image Vis. Comput. 295–306 (1998)Google Scholar
  32. 32.
    Sanders, B.: Our Revolution: A Future to Believe In. Thomas Dunne Books, New York City (2016)Google Scholar
  33. 33.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations 2015 (2015)Google Scholar
  34. 34.
    Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway network. arXiv:1505.00387v2 (2015)
  35. 35.
    Stahl, L.: President-elect trump speaks to a divided country on 60 minutes. CBS (2016)Google Scholar
  36. 36.
    Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with Twitter: what 140 characters reveal about political sentiment. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (2010)Google Scholar
  37. 37.
    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)Google Scholar
  38. 38.
    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)Google Scholar
  39. 39.
    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
  40. 40.
    Zamal, F.A., Liu, W., Ruths, D.: Homophily and latent attribute inference: inferring latent attributes of Twitter users from neighbors. In: Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Laserlike Inc.Mountain ViewUSA
  2. 2.Department of Computer ScienceUniversity of RochesterRochesterUSA
  3. 3.School of PsychologyBeijing Normal UniversityBeijingChina

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