Detecting Hate Speech Against Athletes in Social Media

Abstract

When English clubs and the game’s governing bodies and organizations turned off their Facebook, Twitter, and Instagram accounts from April 30 to May 1, 2021, the fight against online racism regained a new momentum. However, the Tokyo Olympics revealed new aspects of online bullying that athletes may face during major sporting events. Despite the significant effort put into online hate speech detection research in general, hate speech detection against athletes requires a separate investigation. We show in this paper that abusive language directed at athletes is more varied and difficult to detect. We began with the introduction of the collected data from online comments aimed at three athletes competing in the Tokyo Olympics 2020. Followed by conducting an extensive classification experiments of the collected data to demonstrate its diversity in comparison to other hate speech datasets. This was done to demonstrate that Active Learning outperforms Supervised Learning in hate speech detection against athletes.