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

Liye Fu, Cristian Danescu-Niculescu-Mizil, Lillian Lee

Proceedings of the IJCAI workshop on NLP meets Journalism, 2016

Best Paper Award

[PDF] [Data(README)] [Slides] [arXiv] [#CoverTheAthelete]

[New York Times writeup and visualization]

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.

BibTeX

@InProceedings{fu2016tie,
       author = {Liye Fu and Cristian Danescu-Niculescu-Mizil and Lillian Lee},
       title = {Tie-breaker: Using language models to quantify gender bias in sports journalism},
       year = {2016},
       booktitle = {Proceedings of the IJCAI workshop on NLP meets Journalism}
      }