Srikissoon, TrishantaMarivate, Vukosi2024-05-302024-05-302023Srikissoon, T. & Marivate, V. 2023, 'Combating hate : how multilingual transformers can help detect topical hate speech', EPiC SeriesinComputing, vol. 93, pp. 203-215. DOI:10.29007/1cm6.2398-7340 (online)10.29007/1cm6http://hdl.handle.net/2263/96304Automated hate speech detection is important to protecting people’s dignity, online experiences, and physical safety in Society 5.0. Transformers are sophisticated pre-trained language models that can be fine-tuned for multilingual hate speech detection. Many studies consider this application as a binary classification problem. Additionally, research on topical hate speech detection use target-specific datasets containing assertions about a particular group. In this paper we investigate multi-class hate speech detection using target-generic datasets. We assess the performance of mBERT and XLM-RoBERTA on high and low resource languages, with limited sample sizes and class imbalance. We find that our fine-tuned mBERT models are performant in detecting gender-targeted hate speech. Our Urdu classifier produces a 31% lift on the baseline model. We also present a pipeline for processing multilingual datasets for multi-class hate speech detection. Our approach could be used in future works on topically focused hate speech detection for other low resource languages, particularly African languages which remain under-explored in this domain.en© 2023 EasyChair.Hate speechMachine learningNatural language processingSDG-08: Decent work and economic growthArticle