HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter

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. We finally argue that such low performance renders hate speech moderation with public detection models unfeasible, even in a human-in-the-loop setting which we find is prohibitively costly. Overall, we emphasize the need to evaluate future detection models from academia and platforms in real-world settings to address this global challenge.

NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data

To address the global issue of hateful content proliferating in online platforms, hate speech detection (HSD) models are typically developed on datasets collected in the United States, thereby failing to generalize to English dialects from the Majority World. Furthermore, HSD models are often evaluated on curated samples, raising concerns about overestimating model performance in real-world settings. In this work, we introduce NaijaHate, the first dataset annotated for HSD which contains a representative sample of Nigerian tweets. We demonstrate that HSD evaluated on biased datasets traditionally used in the literature largely overestimates real-world performance on representative data. We also propose NaijaXLM-T, a pretrained model tailored to the Nigerian Twitter context, and establish the key role played by domain-adaptive pretraining and finetuning in maximizing HSD performance. Finally, we show that in this context, a human-in-the-loop approach to content moderation where humans review 1% of Nigerian tweets flagged as hateful would enable to moderate 60% of all hateful content. Taken together, these results pave the way towards robust HSD systems and a better protection of social media users from hateful content in low-resource settings.