Affiliation:
1. Indiana University Bloomington, USA
2. University of Minnesota, Minneapolis, USA
Abstract
This study considered the impact of gender on visual coverage of the top 12 candidates in the 2020 Democratic Presidential Primary. Using Microsoft Azure’s Face API, we analyzed 9,529 still images from 43 mainstream news sources for facial emotion (happiness, anger, neutrality) and prominence (close-up, medium, long shots). We found visual evidence for an age-old narrative that undermines confidence in women’s leadership fitness: They were presented as emotionally less composed than men. Although we found no gender differences for facial prominence per se, its interaction with facial emotion gave nuance to gender differences in visual coverage of leadership performances.
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