The keywords for a single frame were then considered a ‘document’; each keyword (which could consist of compounds, e.g., ‘army truck’) was considered a ‘word’. As a control for the method and the automated analysis, we did a close reading of the topics. We then applied the topic model to each list of keywords, resulting in a topic distribution for each frame. For a larger video corpus, each video could be identified by its topic distribution. This makes the videos easily comparable, using established distance metrics, such as PCA or Isomap, to map them (Howanitz, 2020, 92–107).