Automated Coding of Political Video Ads for Political Science Research

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.