  Populist rhetoric employed on online media is characterized as deeply
impassioned and often imbued with strong emotions. The aim of this paper is to
empirically investigate the differences in affective nonverbal communication of
political leaders. We use a deep-learning approach to process a sample of 220
YouTube videos of political leaders from 15 different countries, analyze their
facial expressions of emotion and then examine differences in average emotion
scores representing the relative presence of 6 emotional states (anger,
disgust, fear, happiness, sadness, and surprise) and a neutral expression for
each frame of the YouTube video. Based on a sample of manually coded images, we
find that this deep-learning approach has 53-60\% agreement with human labels.
We observe statistically significant differences in the average score of
negative emotions between groups of leaders with varying degrees of populist
rhetoric.
