Performativity in Politics: Understanding the Role of Affect in Political News Coverage

Abstract

The United States Democratic primaries for the 2020 election kicked off with an incredibly diverse pool of candidates with regards to gender, race, age, and socioeconomic status. However, as the primaries progressed and the pool of candidates narrowed, voters elected to nominate Joe Biden—a white man in his late seventies—to take on Donald Trump in November, 2020. Given the similarity between Elizabeth Warren’s platform and Bernie Sanders’, the purpose of this paper is to explore how news-media coverage contributes to the role of gender in campaigns for president in the United States. Grounded in a theoretical understanding of gender performativity in politics, this study uses a quantitative sentiment analysis of newspaper articles about both candidates to understand whether reporters expressed underlying sentiments differing based on the candidates’ gender. Articles were selected from The New York Times (NYT), The Washington Post (WaPo), National Public Radio (NPR), The Associated Press (AP), and the Wall Street Journal (WSJ) to represent the diversity of reputable, mainstream news outlets considered to have minimal partisan bias available to the American public. Though the sentiment analysis revealed no significant difference in reporting across the different sources by candidate, factors such as rules for news publications and the nuances in political orientation of the two candidates may have limited the role of sentiment in contributing to political gender bias in this case study. This research is of broad interest as it sheds light on the current gendered political landscape in the United States, where a female president has yet to be elected. Furthermore, this study explores the within-party gender dynamics in reporting, in contrast to the myriad studies published in the aftermath of the 2016 presidential election in which Hillary Clinton lost to Donald Trump.