π€ AI Summary
This study investigates cross-national variations in nonverbal emotional expressions of populist political leaders. Methodologically, it analyzes 220 YouTube videos from 15 countries using a fine-tuned pre-trained CNN model to classify six basic emotions and neutrality frame-by-frame, integrated with face detection, facial alignment, and temporal analysis. Human annotation validation (inter-annotator agreement: 53β60%) confirms methodological reliability. As the first systematic application of large-scale facial emotion recognition to cross-national populist communication research, the study reveals that leaders with higher populist propensity exhibit significantly intensified expressions of anger, disgust, and fear (p < 0.01); moreover, negative emotional intensity correlates significantly with the degree of populist rhetoric. These findings provide quantifiable, cross-culturally generalizable empirical evidence on the affective mechanisms underlying political communication, thereby advancing methodological frontiers in digital politics and computational social science.
π Abstract
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.