Facing the Electorate: Computational Approaches to the Study of Nonverbal Communication and Voter Impression Formation

Politicians have strong incentives to use their communication to positively impress and persuade voters. Yet one important question that persists within the fields of political science, communication, and psychology is whether appearance or substance matters more during political campaigns. To a large extent, this appearance vs. substance question remains open and, crucially, the notion that appearance can in fact effectively sway voter perceptions is consequential for the health of democracy. This study leverages advances from the fields of machine learning and computer vision to expand our knowledge on how nonverbal elements of political communication influence voters immediate impressions of political actors. We rely on video from the 4th Republican Party presidential debate held on November 10, 2016, as well as continuous response approval data from a live focus group (n = 311; 36,528 reactions), to determine how the emotional displays of political candidates influence voter impression formation. Our results suggest that anger displays can positively influence viewers' real-time evaluations. Happiness displays, on the other hand, are much less effective in eliciting a response from the viewing public, while fear displays were rarely projected by the candidates of the debate under study.