  In this paper, we seek to understand how politicians use images to express
ideological rhetoric through Facebook images posted by members of the U.S.
House and Senate. In the era of social media, politics has become saturated
with imagery, a potent and emotionally salient form of political rhetoric which
has been used by politicians and political organizations to influence public
sentiment and voting behavior for well over a century. To date, however, little
is known about how images are used as political rhetoric. Using deep learning
techniques to automatically predict Republican or Democratic party affiliation
solely from the Facebook photographs of the members of the 114th U.S. Congress,
we demonstrate that predicted class probabilities from our model function as an
accurate proxy of the political ideology of images along a left-right
(liberal-conservative) dimension. After controlling for the gender and race of
politicians, our method achieves an accuracy of 59.28% from single photographs
and 82.35% when aggregating scores from multiple photographs (up to 150) of the
same person. To better understand image content distinguishing liberal from
conservative images, we also perform in-depth content analyses of the
photographs. Our findings suggest that conservatives tend to use more images
supporting status quo political institutions and hierarchy maintenance,
featuring individuals from dominant social groups, and displaying greater
happiness than liberals.
