The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning

Online media has revolutionized the way political information is disseminated and consumed on a global scale, and this shift has compelled political figures to adopt new strategies of capturing and retaining voter attention. These strategies often rely on emotional persuasion and appeal, and as visual content becomes increasingly prevalent in virtual space, much of political communication too has come to be marked by evocative video content and imagery. The present paper offers a novel approach to analyzing material of this kind. We apply a deep-learning-based computer-vision algorithm to a sample of 220 YouTube videos depicting political leaders from 15 different countries, which is based on an existing trained convolutional neural network architecture provided by the Python library fer. The algorithm returns 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 processed YouTube video. We observe statistically significant differences in the average score of expressed negative emotions between groups of leaders with varying degrees of populist rhetoric as defined by the Global Party Survey (GPS), indicating that populist leaders tend to express negative emotions to a greater extent during their public performance than their non-populist counterparts. Overall, our contribution provides insight into the characteristics of visual self-representation among political leaders, as well as an open-source workflow for further computational studies of their non-verbal communication.