Inferring Follower Preferences in the 2016 U.S. Presidential Primaries with Sparse Learning

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


In this paper, we propose a framework to infer Twitter follower preferences for the 2016 U.S. presidential primaries. Using Twitter data collected from Sept. 2015 to Mar. 2016, we first uncover the tweeting tactics of the candidates and then exploit the variations in the number of ‘likes’ to infer followers’ preference. With sparse learning, we are able to reveal neutral topics as well as positive and negative ones.

Methodologically, we are able to achieve a higher predictive power with sparse learning. Substantively, we show that for Hillary Clinton the (only) positive issue area is women’s rights. We demonstrate that Hillary Clinton’s tactic of linking herself to President Obama resonates well with her supporters but the same is not true for Bernie Sanders. In addition, we show that Donald Trump is a major topic for all the other candidates, and that the women’s rights issue is equally emphasized in Sanders’ campaign as in Clinton’s.

Lessons from the primaries can help inform the general election and beyond. We suggest two ways that politicians can use the feedback mechanism in social media to improve their campaign: (1) use feedback from social media to improve campaign tactics within social media; (2) formulate policies and test the public response from the social media.


Presidential primaries Republicans Democrats Preference Twitter Sparse learning 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of RochesterRochesterUSA
  2. 2.School of PsychologyBeijing Normal UniversityBeijingChina

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