What data is used in this work?
post-game interviews from ASAP Sport's website
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.
There has been an increasing level of attention to and discussion of gender bias in sports, ranging from differences in pay and prize money to different levels of focus on off-court topics in interviews by journalists. With respect to the latter, Cover the Athlete, an initiative that urges the media to focus on sport performance, suggests that female athletes tend to get more “sexist commentary" and “inappropriate interview questions" than males do; the organization put out an attention-getting video in 2015 purportedly showing male athletes' awkward reactions to receiving questions like those asked of female athletes. However, it is not universally acknowledged that female athletes attract more attention for off-court activities. For instance, a manual analysis by BIBREF0 [ BIBREF0 ] of online articles revealed significantly more descriptors associated with the physical appearance and personal lives of male basketball players in comparison to female ones. Transcripts of pre- or post-game press conferences offer an opportunity to determine quantitatively and in a data-driven manner how different are the questions which journalists pose to male players from those they pose to female players. Here are examples of a game-related and a non-game-relevant question, respectively, drawn from actual tennis interviews: To quantify gender discrepancies in questions, we propose a statistical language-model-based approach to measure how game-related questions are. In order to make such an approach effective, we restrict our attention in this study to a single sport—tennis—so that mere variations in the lingo of different sports do not introduce extra noise in our language models. Tennis is also useful for our investigation because, as BIBREF1 [ BIBREF1 ] noted, it “marks the only professional sports where male and female athletes generally receive similar amounts of overall broadcast media coverage during the major tournaments." Using our methodology, we are able to quantify gender bias with respect to how game-related interview questions are. We also provide a more fine-grained analysis of how gender differences in journalistic questioning are displayed under various scenarios. To help with further analysis of interview questions and answers, we introduce a dataset of tennis post-match interview transcripts along with corresponding match information.