  Gender is playing an important role in the 2016 U.S. presidential election,
especially with Hillary Clinton becoming the first female presidential nominee
and Donald Trump being frequently accused of sexism. In this paper, we
introduce computer vision to the study of gender politics and present an
image-driven method that can measure the effects of gender in an accurate and
timely manner. We first collect all the profile images of the candidates'
Twitter followers. Then we train a convolutional neural network using images
that contain gender labels. Lastly, we classify all the follower and unfollower
images. Through two case studies, one on the `woman card' controversy and one
on Sanders followers, we demonstrate how gender is informing the 2016
presidential election. Our framework of analysis can be readily generalized to
other case studies and elections.
