  While members of Congress now routinely communicate with constituents using
images on a variety of internet platforms, little is known about how images are
used as a means of strategic political communication. This is due primarily to
computational limitations which have prevented large-scale, systematic analyses
of image features. New developments in computer vision, however, are bringing
the systematic study of images within reach. Here, we develop a framework for
understanding visual political communication by extending Fenno's analysis of
home style (Fenno 1978) to images and introduce "photographic" home styles.
Using approximately 192,000 photographs collected from MCs Facebook profiles,
we build machine learning software with convolutional neural networks and
conduct an image manipulation experiment to explore how the race of people that
MCs pose with shape photographic home styles. We find evidence that electoral
pressures shape photographic home styles and demonstrate that Democratic and
Republican members of Congress use images in very different ways.
