Pricing the Woman Card: Gender Politics between Hillary Clinton and Donald Trump
arXiv:1605.05401
Abstract
In this paper, we propose a data-driven method to measure the impact of the 'woman card' exchange between Hillary Clinton and Donald Trump. Building from a unique dataset of the two candidates' Twitter followers, we first examine the transition dynamics of the two candidates' Twitter followers one week before the exchange and one week after. Then we train a convolutional neural network to classify the gender of the followers and unfollowers, and study how women in particular are reacting to the 'woman card' exchange. Our study suggests that the 'woman card' comment has made women more likely to follow Hillary Clinton, less likely to unfollow her and that it has apparently not affected the gender composition of Trump followers.
4 pages, 6 figures, 7 tables, under review
References in corpus (4)
- Deciphering the 2016 U.S. Presidential Campaign in the Twitter Sphere: A Comparison of the Trumpists and Clintonists
- Catching Fire via "Likes": Inferring Topic Preferences of Trump Followers on Twitter
- A Century of Portraits: A Visual Historical Record of American High School Yearbooks
- To Follow or Not to Follow: Analyzing the Growth Patterns of the Trumpists on Twitter