A big data analysis of social media coverage of athlete protests

Abstract

ABSTRACT Using a contentious issue in sport – the athlete protests during the playing of the national anthem – this paper examined the relationship between media outlets’ social media coverage of athlete protests and the social media user interest and sentiment. We analysed data sourced from the media outlets’ official Instagram accounts, along with comments on these posts. Using both sentiment lexicons and Random Forrest machine learning models, we derived the sentiment of 496 official Instagram posts and 137,735 user comments. We utilised logit and ordered logit regressions to examine whether media coverage of the athlete protests was responsive to user interest and user sentiment towards the issue. In addition, we employed multinomial logit regressions and two-stage least squared regressions to investigate media’s selection of topics and portrayal of the protests. We found strong evidence that both media’s decisions to cover the protests and how they cover the issue are sensitive to social media user interest and sentiment. HIGHLIGHTS Test the relationship between social media coverage of athlete protests and social media user interest and sentiment. Media coverage of the protests was sensitive to social media user interest and sentiment. Media outlets were more likely to cover topics at the intersection of sport and politics when user sentiment towards the protests was negative. When there was increased social media interest media outlets tend to use more negative tones to cover the protests.