Same Candidates, Different Faces: Uncovering Media Bias in Visual Portrayals of Presidential Candidates with Computer Vision

How do today’s partisan media outlets produce ideological bias in their visual coverage of political candidates? Applying computer vision techniques, this study examined 13,026 images from 15 news websites about the two candidates in the 2016 U.S. presidential election. The analysis unveils a set of visual attributes (e.g., facial expressions, face size, skin condition) that were adopted by media outlets of varying ideologies to differentially portray these two candidates. In addition, this study recruited 596 crowdsourced workers to rate a subset of 1,200 images and demonstrated that some visual features also effectively shape viewers’ perceptions of media slant and impressions of the candidates. For example, Clinton was portrayed with more expressions of happiness, which rendered her as more favorable, whereas Trump was associated with more expressions of anger, which made him look less positive but more dominant. These differences in facial expressions varied in line with media outlets’ political leanings.