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:在线媒体革命性地改变了全球政治信息的传播和消费方式,这种转变促使政治人物采用新的策略来捕获和保持选民的关注。这些策略往往依赖于情感说服和吸引,并且随着视觉内容在虚拟空间中变得越来越普遍,大部分政治沟通也被标记为有感染力的视频内容和图像。本文提供了一种分析此类材料的新方法。我们将基于现有训练好的卷积神经网络架构,应用深度学习为基础的计算机视觉算法到220个展示了来自15个不同国家的政治领袖的YouTube视频样本中,该算法基于Python库fer提供。该算法返回情感分数,代表处理的每个YouTube视频帧中6种情感状态(愤怒、厌恶、恐惧、快乐、悲伤和惊讶)和一种中性表情的相对存在。我们观察到,在有不同民粹主义措辞的领导人组之间,表达了负面情绪的平均分数存在统计学意义,这表明民粹主义领袖在公开表现中比非民粹主义领袖更倾向于表达负面情绪。总体而言,我们的贡献提供了关于政治领袖视觉自我表现的特征的见解,以及一个开源的工作流,用于进一步的计算研究其非语言沟通。