Understanding Gender Stereotypes and Electoral Success from Visual Self-presentations of Politicians in Social Media

Abstract

Social media have been widely used as a platform for political communication, promoting firsthand dialogue between politicians and the public. This paper studies the role of visual self-presentation in social media in political campaigns with a primary focus on gender stereotypical cues exhibited in Facebook timeline posts of 562 candidates in the 2018 U.S. general elections. We train a convolutional neural network (CNN) that infers gender stereotypes from the photographs based on crowdsourced annotations. Using regression analysis, we find that masculine traits are predictive factors for winning elections for both gender and parties. In contrast, feminine traits are not correlated with electoral success. Prediction experiments show that the visual traits on gender stereotypes can predict the election outcomes with an accuracy of 0.739, which was better than the performance (0.724) of making a direct prediction from the raw photographs. Our study demonstrates that the automated visual content analysis can reliably measure subtle, emotional, and subjective personal trait dimensions from political images, thereby enabling systematic investigations on multi-modal political communication via social media.