The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning
This addresses the problem of understanding populist rhetoric in online media for political science and communication researchers, but it is incremental as it applies existing methods to a new dataset.
This paper tackled the problem of empirically investigating differences in affective nonverbal communication of political leaders by analyzing facial emotional expressions in YouTube videos using a deep-learning approach, finding statistically significant differences in negative emotions between leaders with varying degrees of populist rhetoric.
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.