Aimed emotions in American presidential politics

Abstract

ABSTRACT Emotional appeals are essential tools for political candidates to motivate supporters, donors, and voters. Prior research has demonstrated the distinct behavioral consequences of discrete emotions, such as anger, anxiety, and enthusiasm. Do political candidates take advantage of these distinctions in their communication strategies? In this paper, I use supervised machine learning to classify emotional content in debate transcripts and contemporaneous tweets of American presidential candidates in the 2016 and 2020 elections, and show that candidates preference different emotional appeals in each communication medium. I argue that this behavior enables candidates to reap strategic benefits from two dissimilar audiences simultaneously.