Replication Data for: Automated Coding of Political Campaign Advertisement Videos: An Empirical Validation Study

Video advertisements, either through television or the Internet, play an essential role in modern political campaigns. For over two decades, researchers have studied television video ads by analyzing the hand-coded data from the Wisconsin Advertising Project and its successor, the Wesleyan Media Project (WMP). Unfortunately, manually coding more than a hundred of variables, such as issue mentions, opponent appearance, and negativity, for many videos is a laborious and expensive process. We propose to automatically code campaign advertisement videos. Applying state-of-the-art machine learning methods, we extract various audio and image features from each video file. We show that our machine coding is comparable to human coding for many variables of the WMP data sets. Since many candidates make their advertisement videos available on the Internet, automated coding can dramatically improve the efficiency and scope of campaign advertisement research. Open-source software package is available for implementing the proposed methodology.

无论是电视端还是网络端的视频广告，在现代政治竞选活动中均发挥着不可或缺的重要作用。二十余年来，研究者依托威斯康星广告项目（Wisconsin Advertising Project）及其后续项目卫斯理传媒项目（Wesleyan Media Project, WMP）的人工编码数据，开展了大量电视视频广告相关研究。然而，针对海量视频手动编码上百项变量——诸如议题提及、对手出镜、负面倾向等——是一项耗时耗力且成本高昂的工作。为此，我们提出对竞选宣传视频进行自动编码的方案：采用当前最先进的机器学习方法，从每个视频文件中提取各类音频与图像特征。实验结果表明，针对卫斯理传媒项目数据集的多数变量，我们的机器编码结果可与人工编码相媲美。鉴于诸多竞选候选人都会将其广告视频上传至互联网，自动编码技术可显著提升竞选广告研究的效率与研究范围。现已推出可实现本研究方法的开源软件包。