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：在线媒体已在全球范围内彻底改变了政治信息的传播与消费方式，这一转变迫使政治人物采用新策略来吸引并维持选民注意力。这些策略往往依赖情感说服与感染力，随着视觉内容在虚拟空间中日益盛行，大量政治传播也呈现出富有感染力的视频内容与影像特征。本文为分析此类素材提供了一种新方法：我们基于Python库fer提供的预训练卷积神经网络架构，对来自15个国家的政治领袖的220个YouTube视频样本应用了深度学习计算机视觉算法。该算法返回代表6种情绪状态（愤怒、厌恶、恐惧、快乐、悲伤、惊讶）及中性表情的相对情感评分，每个视频帧均对应一组分数。根据全球政党调查（GPS）界定的民粹主义修辞程度，不同组别领袖所表达负面情绪的平均得分存在统计学显著差异——这表明民粹主义领袖在公开表现中比非民粹主义同行更倾向于表达负面情绪。总体而言，我们的研究揭示了政治领袖视觉自我呈现的特征，并为其非语言传播的进一步计算研究提供了开源工作流程。