  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.
