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

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  • Sara Major

    (University of Novi Sad)

  • Aleksandar Tomašević

    (University of Novi Sad)

Abstract

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.

Suggested Citation

  • Sara Major & Aleksandar Tomašević, 2025. "The face of populism: examining differences in facial emotional expressions of political leaders using machine learning," Journal of Computational Social Science, Springer, vol. 8(3), pages 1-22, August.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:3:d:10.1007_s42001-025-00392-w
    DOI: 10.1007/s42001-025-00392-w
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    References listed on IDEAS

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    1. Benjamin Moffitt & Simon Tormey, 2014. "Rethinking Populism: Politics, Mediatisation and Political Style," Political Studies, Political Studies Association, vol. 62(2), pages 381-397, June.
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    3. Jankowski, Michael & Huber, Robert A., 2023. "When Correlation Is Not Enough: Validating Populism Scores from Supervised Machine-Learning Models," Political Analysis, Cambridge University Press, vol. 31(4), pages 591-605, October.
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