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.