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

arXiv: https://arxiv.org/abs/2304.09914 · HTML: https://mlsanity.com/paper/2304.09914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 93 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text](https://mlsanity.com/paper/2005.11882/) (2020)
- [Real-time Convolutional Neural Networks for Emotion and Gender Classification](https://mlsanity.com/paper/1710.07557/) (2017)
- [Facial Emotion Recognition: State of the Art Performance on FER2013](https://mlsanity.com/paper/2105.03588/) (2021)
- [ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1309.3295/) (2013)
- [Perception for Autonomous Systems (PAZ)](https://mlsanity.com/paper/2010.14541/) (2020)
- [Auditing the Biases Enacted by YouTube for Political Topics in Germany](https://mlsanity.com/paper/2107.09922/) (2021)

## Abstract (arXiv)

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.