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.

翻译：在线媒体已彻底改变了政治信息在全球范围内的传播与接收方式，这一转变迫使政治人物采用新策略来吸引和维持选民注意力。这些策略常依赖情绪说服与感染力，随着视觉内容在虚拟空间中日益占据主导地位，大量政治传播也开始以富有感染力的视频内容和图像为标志。本文提出了一种分析此类素材的新方法。我们基于Python库fer提供的预训练卷积神经网络架构，将基于深度学习的计算机视觉算法应用于来自15个国家的220个政治领袖YouTube视频样本。该算法为每个视频帧返回代表6种情绪状态（愤怒、厌恶、恐惧、幸福、悲伤、惊讶）及中性表情的相对得分。我们观察到，根据全球政党调查（GPS）定义的民粹主义修辞程度不同的领袖群体之间，其表达负面情绪的平均得分存在统计显著性差异，表明民粹主义领袖在公共表现中比非民粹主义领袖更倾向外显负面情绪。总体而言，本研究不仅揭示了政治领袖视觉自我呈现的特征，还提供了可复现的开源工作流，以推进对其非语言沟通的后续计算研究。