Can training police officers on how to best interact with the public actually improve their interactions with community members? This has been a challenging question to answer. Interpersonal aspects of policing are consequential but largely invisible in administrative records commonly used for evaluation. In this study, we offer a solution: body-worn camera footage captures police-community interactions and how they might change as a function of training. Using this footage-as-data approach, we consider changes in officers' communication following procedural justice training in Oakland, CA, USA, one module of which sought to increase officer-communicated respect during traffic stops. We applied natural language processing tools and expert annotations of traffic stop recordings to detect whether officers enacted the five behaviors recommended in this module. Compared with recordings of stops that occurred prior to the training, we find that officers employed more of these techniques in posttraining stops; officers were more likely to express concern for drivers' safety, offer reassurance, and provide explicit reasons for the stop. These methods demonstrate the promise of a footage-as-data approach to capture and affect change in police-community interactions.
