PALADIN: Understanding Video Intentions in Political Advertisement Videos

In this paper, we introduce a novel task for video understanding that focuses on detecting editing intentions in political advertisement videos. Political advertisement videos are edited with some intentions (e.g., ``associating some candidates with negative emotions’’) of making people unthinkingly believe the messages in the videos, potentially ending up with some irrational bias. Detecting such intentions is thus the primary step toward fairer decision-making based on the messages themselves. To this end, we classify such editing intentions into 10 categories (referred to as communication techniques) in consultation with a professional editor as well as based on communication techniques presented in the natural language processing community, and build a dataset of 12,526 political advertisement videos, each of which are annotated with several communication technique segments. We also explore the capability of existing video understanding models in detecting editing intentions over the dataset, which identifies new dimensions of challenges to be addressed.