Abstract: To tackle the global challenge of online hate speech, a large body of research has developed detection models to flag hate speech in the sea of online content. Yet, due to systematic biases in evaluation datasets, detection performance in real-world settings remains unclear, let alone across geographies. To address this issue, we introduce HateDay, the first global hate speech dataset representative of social media settings, randomly sampled from all tweets posted on September 21, 2022 for eight languages and four English-speaking countries. Using HateDay, we show how the prevalence and composition of hate speech varies across languages and countries. We also find that evaluation on academic hate speech datasets overestimates real-world detection performance, which we find is very low, especially for non-European languages. We identify several factors explaining poor performance, including models' inability to distinguish between hate and offensive speech, and the misalignment between academic target focus and real-world target prevalence. Overall, we emphasize the need to evaluate future detection models from academia and platforms in real-world settings to address this global challenge.
Bio: Manuel Tonneau is a third year Ph.D. student in Social Data Science and a Shirley Scholar at the Oxford Internet Institute of the University of Oxford. His research is at the intersection of natural language processing (NLP) and AI ethics, aiming to create inclusive NLP systems that work across cultures without perpetuating societal harms. As part of his PhD, Manuel focuses on AI-driven moderation of hate speech on social media, investigating how these systems may unequally protect users from online hate across cultures. Additionally, Manuel works on identifying and reducing harms in text generated by large language models, with a particular emphasis on Global Majority contexts. He holds an Engineering degree in Statistics and Economics (eq. to MSc) from ENSAE Paris as well as an MSc in Economics from Humboldt-Universität zu Berlin.