FALL 2023

AUTHOR

Nathaniel Yellin, Leffell School

ABSTRACT

In this journal article, I investigate gender bias in sports journalism, focusing on ESPN.com coverage of National Collegiate Athletic Association (NCAA) basketball. With the programming language R and using natural language processing (NLP) techniques, I analyze more than 1,700 articles on ESPN.com to create an R Shiny application Sidelined. This app reveals that gender bias is prevalent in online NCAA basketball coverage at the league, team, and individual player level. At the league level, I find a significant quantitative difference in female coverage, in the number of articles, game recaps and correct assignment of articles on ESPN.com for the women’s teams and players as compared to male counterparts. Being mentioned in one article is considered an outlier for female athletes. I also uncover that male players who play and score more receive more media attention, but this relationship is less clear for female players.

Performing sentiment analysis on the adjectives used to describe NCAA athletes, I reveal that there is a notable qualitative difference in how female NCAA athletes are covered, especially those who play the center position. Sidelined is innovative and can be explored to investigate, with data visualizations and word clouds, the extent to which gender bias exists on ESPN.com at the specific team or player level. The findings of this research are significant, highlighting the need for greater attention to gender bias in online sports journalism and the potential for NLP to play a key role in identifying and addressing such biases.