Mad, sad, but mostly glad: how men and women in politics communicate using emotions in images

Political leaders use emotions to attract support from voters, take positions on issues or policies, and enhance communication. Yet men and women are constrained by gender role expectations, which limit the range and type of acceptable emotions that a leader can express. In this paper, we examine how gender shapes the political communication of emotion through a new measure: emotional expression in images posted by leaders on social media. We examine more than 450,000 Facebook posts by Members of Congress (MOCs), extracting images from these posts, faces from those images, and identifying MOCs faces out to identify when MOCs post images of themselves as angry, happy, or expressing any emotion at all. Specifically, we develop a custom facial recognition model to identify MOCs in their social media imagery and use a convolutional neural network to detect emotional displays. Drawing on research on role congruity, we argue (and find) that women MOCs will post more images of themselves being happy and expressing any emotion and fewer images of themselves as angry. We then use the source of these images (looking at the MOC herself versus other MOCs) to show that these differences originate in broader gendered patterns, rather than just personalization and self-presentation. The project offers an opportunity to understand how political leaders use emotions and images to communicate to constituents and how gender shapes these actions.