Tactics and Tallies: Inferring Voter Preferences in the 2016 U.S. Presidential Primaries Using Sparse Learning

TL;DR

This study uses Twitter data and sparse learning to infer voter preferences and campaign tactics during the 2016 U.S. presidential primaries, revealing key issue areas and candidate strategies.

Contribution

It introduces a web-centered framework combining social media analysis and sparse learning to infer voter preferences and campaign tactics.

Findings

Hillary Clinton's positive issue focus is on women's rights.

Trump is a major topic across campaigns.

Clinton's tactic of linking to Obama resonates with supporters.

Abstract

In this paper, we propose a web-centered framework to infer voter preferences for the 2016 U.S. presidential primaries. Using Twitter data collected from Sept. 2015 to March 2016, we first uncover the tweeting tactics of the candidates and then exploit the variations in the number of 'likes' to infer voters' 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.