Tie-breaker: Using language models to quantify gender bias in sports journalism

TL;DR

This paper introduces a language-model-based method to quantify gender bias in sports journalism, revealing that questions to male athletes tend to focus more on the game than those to female athletes, with bias influenced by multiple factors.

Contribution

It presents a novel approach using language models to measure gender bias in sports interviews and provides detailed analysis of factors affecting this bias.

Findings

Questions to male athletes focus more on the game.

Bias varies with question type, game outcome, and player rank.

Language models effectively quantify gender bias in sports journalism.

Abstract

Gender bias is an increasingly important issue in sports journalism. In this work, we propose a language-model-based approach to quantify differences in questions posed to female vs. male athletes, and apply it to tennis post-match interviews. We find that journalists ask male players questions that are generally more focused on the game when compared with the questions they ask their female counterparts. We also provide a fine-grained analysis of the extent to which the salience of this bias depends on various factors, such as question type, game outcome or player rank.