Muhammad,S.H.Abdulmumin,I.Ayele,A.A.Adelani,D.I.Ahmad,I.S.Aliyu,S.M.Onyango,N.O.Wanzare,L.D.A.Rutunda,S.Aliyu,L.J.Alemneh,E.Hourrane,O.Gebremichael,H.T.Ismail,E.A.Beloucif,M.Jibril,E.C.Bukula,A.Mabuya,R.Osei, SalomeyOppong,A.Belay,T.D.Guge,T.K.Asfaw,T.T.Chukwuneke,C.I.Röttger,P.Yimam,S.M.Ousidhoum,N.2026-04-202026-04-202025Muhammad, S. H., Abdulmumin, I., Ayele, A. A., Adelani, D. I., Ahmad, I. S., Aliyu, S. M., Onyango, N. O., Wanzare, L. D. A., Rutunda, S., Aliyu, L. J., Alemneh, E., Hourrane, O., Gebremichael, H. T., Ismail, E. A., Beloucif, M., Jibril, E. C., Bukula, A., Mabuya, R., Osei, S., et al. (2025). AFRIHATE: a multilingual collection of hate speech and abusive language datasets for African languages. Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies: Long Papers, NAACL-HLT 2025, 1, 1854-1871. https://doi.org/10.18653/V1/2025.NAACL-LONG.92979889176189610.18653/V1/2025.NAACL-LONG.92https://hdl.handle.net/20.500.14454/5694Ponencia presentada en la 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics, celebrada en Albuquerque entre el 29 de abril y el 4 de mayo de 2025Hate speech and abusive language are global phenomena that need socio-cultural background knowledge to be understood, identified, and moderated. However, in many regions of the Global South, there have been several documented occurrences of (1) absence of moderation and (2) censorship due to the reliance on keyword spotting out of context. Further, high-profile individuals have frequently been at the center of the moderation process, while large and targeted hate speech campaigns against minorities have been overlooked. These limitations are mainly due to the lack of high-quality data in the local languages and the failure to include local communities in the collection, annotation, and moderation processes. To address this issue, we present AFRIHATE: a multilingual collection of hate speech and abusive language datasets in 15 African languages, annotated by native speakers. We report the challenges related to the construction of the datasets and present various classification baseline results with and without using LLMs. We find that model performance highly depends on the language and that multilingual models can help boost the performance in low-resource settings.eng©2025 Association for Computational Linguisticsconference paper2026-04-20