Abstract: Police body-worn cameras have the potential to play an important role in understanding and improving police-community relations. In this talk I describe a series of studies conducted by our large interdisciplinary team at Stanford that use speech and natural language processing on body-camera recordings to model the interactions between police officers and community members in traffic stops. We draw on linguistic models of dialogue structure and of interpersonal relations like respect to automatically quantify linguistic aspects of the interaction from the text and audio. I describe the differences we find in the language directed toward black versus white community members, and offer suggestions for how these findings can be used to help improve the fraught relations between police officers and the communities they serve. I'll also cover a number of our results on using computational methods to uncover historical societal biases, and detect framing, agenda-setting and political polarization in the media. Together, these studies highlight how computational linguistic methods can help us interpret latent social content behind the words we use.