Kicking Prejudice: Large Language Models for Racism Classification in Soccer Discourse on Social Media

In the dynamic space of Twitter, now called X, interpersonal racism surfaces when individuals from dominant racial groups engage in behaviours that diminish and harm individuals from other racial groups. It can be manifested in various forms, including pejorative name-calling, racial slurs, stereotyping, and microaggressions. The consequences of racist speech on social media are profound, perpetuating social division, reinforcing systemic inequalities, and undermining community cohesion. In the specific context of football discourse, instances of racism and hate crimes are well-documented. Regrettably, this issue has seamlessly migrated to the football discourse on social media platforms, especially Twitter. The debate on Internet freedom and social media moderation intensifies, balancing the right to freedom of expression against the imperative to protect individuals and groups from harm. In this paper, we address the challenge of detecting racism on Twitter in the context of football by using Large Language Models (LLMs). We fine-tuned different BERT-based model architectures to classify racist content in the Twitter discourse surrounding the UEFA European Football Championships. The study aims to contribute insights into the nuanced language of hate speech in soccer discussions on Twitter while underscoring the necessity for context-sensitive model training and evaluation. Additionally, Explainable Artificial Intelligence (XAI) techniques, specifically the Integrated Gradient method, are used to enhance transparency and interpretability in the decision-making processes of the LLMs, offering a comprehensive approach to mitigating racism and offensive language in online sports discourses.

http://dx.doi.org/10.1007/978-3-031-61057-8_32