Tie-breaker: Using language models to quantify gender bias in sports journalism
This work addresses gender bias in sports journalism, providing a tool for analysis, but it is incremental as it applies existing language models to a specific domain.
The researchers tackled gender bias in sports journalism by developing a language-model-based method to quantify differences in questions asked to female versus male athletes in tennis post-match interviews, finding that male players receive more game-focused questions.
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.