Automated Coding of Political Video Ads for Political Science Research
Automated Coding of Political Video Ads for Political Science Research
复制标题
DOI：
10.1109/ism.2016.0012
复制
发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
Lei Qi;Chuanhai Zhang;Adisak Sukul;Wallapak Tavanapong;David A. M. Peterson
中科院分区：
文献类型：
--
作者：
Lei Qi;Chuanhai Zhang;Adisak Sukul;Wallapak Tavanapong;David A. M. Peterson
随着新媒体技术的出现和识别更多潜在选民信息的能力，政治竞选活动积极改变了他们的竞选策略。竞选活动越来越依赖在线视频广告来接触选民。到目前为止，这些广告的内容都是人工编码的，用于政治科学研究，以研究竞选策略。手动编码非常耗时，并且无法扩展以处理在线广告的预期增长。我们首次尝试研究政治视频广告内容的自动编码，用于政治学研究。具体来说，我们专注于将政治广告分类为以下类别之一的问题：攻击广告，促销广告和对比广告。我们与领域专家一起为每个类别引入具体定义。我们提供了2016年总统初选的773个政治广告的地面真相标签。我们调查的有效性，使用单模态和两个模态的几个分类。使用来自音频的文本特征和图像帧中的嵌入文本，最佳平均F1得分为0.845。
With the advent of new media technology and the ability to identify more information about potential voters, political campaigns have aggressively changed their campaign strategies. Election campaigns increasingly rely on online video advertising to reach voters. Until now, the contents of these ads are manually coded for political science research to study campaign strategies. Manual coding is tremendously time consuming and not scalable to handle the expected increase in online ads. We make the first attempt to investigate automated coding of the content of political video ads for political science research. Specifically, we focus on the problem of classifying a political ad into one of these categories: attack ads, promoting ads, and contrast ads. Together with the domain expert, we introduce a concrete definition for each of these categories. We made available the ground truth labels of 773 political ads of the 2016 primary presidential election. We investigate the effectiveness of several classifiers using single modality and two modalities. The best average F1 score is 0.845 using text features from audio and embedded text in image frames.