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：在线媒体在全球范围内彻底改变了政治信息的传播与接收方式，这一转变迫使政治人物采取新策略来吸引并维持选民的注意力。这些策略往往依赖于情感说服与诉求，随着视觉内容在虚拟空间中日益普及，大部分政治传播也呈现出以富有感染力的视频内容和图像为特征的趋势。本文提供了一种分析此类素材的新方法。我们将基于深度学习的计算机视觉算法应用于一个包含15个国家政治领导人220个YouTube视频样本中，该算法基于Python库fer提供的现有预训练卷积神经网络架构。算法会返回代表6种情绪状态（愤怒、厌恶、恐惧、快乐、悲伤和惊讶）及中性表情的相对情感得分，针对处理后的YouTube视频每一帧进行计算。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导人群体之间，其表达的负面情绪平均得分存在统计上的显著差异，这表明民粹主义领导人在公开表现中往往比非民粹主义同行更强烈地表达负面情绪。总体而言，我们的研究贡献在于揭示了政治领导人视觉自我呈现的特征，并提供了一个开源工作流程，用于进一步研究他们非语言沟通的计算分析。