Fight or Flight on Fox?: Partisan Fear Responses on U.S. Cable News Shows

Abstract

Empirical work on political communication has so far left out a potentially pivotal dimension – the unspoken emotional responses indicated by facial expressions. This paper shows how to measure these responses using deep-learning-based computer-vision algorithms in the context of U.S. cable news video. Using machine-generated metrics for expressed emotion, combined with mentions of politically divisive entities, we estimate the difference in emotion when cable-channel speakers hear mentions of entities that are from the opposing side of the political divide. We find that the most responsive emotion is fear: When Fox News personalities hear about Democratic entities, or when MSNBC personalities hear about Republican entities, the dominant expressed emotion in their faces is fear, which increases by at least 30% relative to mentions of neutral political entities or those from one’s own partisan team. We find some evidence that this partisan fear response is stronger on Fox News than on MSNBC.

Keywords: Emotions, measuring emotions, cable news, American politics, deep learning, computer vision, data science