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<p>Populist rhetoric employed in online media is characterized as deeply impassioned and most often imbued with strong emotions. This paper investigates the differences in affective non-verbal communication of political leaders. We use a deep-learning approach to process a sample of 220 YouTube videos depicting 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 six emotional states (anger, disgust, fear, happiness, sadness, and surprise) and a neutral expression for each frame of the processed YouTube video. Based on a sample of manually coded images, we find that this machine learning approach has 53−60% agreement with human annotation. We observe statistically significant differences in the average score of expressed negative emotions between groups of leaders with varying degrees of populist rhetoric. Overall, our contribution provides insight into the characteristics of non-verbal emotional expression among political leaders, as well as an open-source workflow for further computational studies of their affective communication.</p>

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The face of populism: examining differences in facial emotional expressions of political leaders using machine learning

  • 期刊论文

  • 发表日期：

    2025年6月4日

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  • Sara Major,
  • Aleksandar Tomašević

摘要

Populist rhetoric employed in online media is characterized as deeply impassioned and most often imbued with strong emotions. This paper investigates the differences in affective non-verbal communication of political leaders. We use a deep-learning approach to process a sample of 220 YouTube videos depicting 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 six emotional states (anger, disgust, fear, happiness, sadness, and surprise) and a neutral expression for each frame of the processed YouTube video. Based on a sample of manually coded images, we find that this machine learning approach has 53−60% agreement with human annotation. We observe statistically significant differences in the average score of expressed negative emotions between groups of leaders with varying degrees of populist rhetoric. Overall, our contribution provides insight into the characteristics of non-verbal emotional expression among political leaders, as well as an open-source workflow for further computational studies of their affective communication.