AfriHate: A Multilingual Collection of Hate Speech and Abusive Language
  Datasets for African Languages
Hate 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. Each instance in AfriHate is annotated by
native speakers familiar with the local culture. We report the challenges
related to the construction of the datasets and present various classification
baseline results with and without using LLMs. The datasets, individual
annotations, and hate speech and offensive language lexicons are available on
https://github.com/AfriHate/AfriHate
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Shamsuddeen Hassan Muhammad
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Idris Abdulmumin
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Idris Abdulmumin
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Abinew Ali Ayele
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David Ifeoluwa Adelani
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Ibrahim Said Ahmad
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Ibrahim Said Ahmad
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Saminu Mohammad Aliyu
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Saminu Mohammad Aliyu
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Nelson Odhiambo Onyango
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Lilian D. A. Wanzare
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Samuel Rutunda
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Samuel Rutunda
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Lukman Jibril Aliyu
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Lukman Jibril Aliyu
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Esubalew Alemneh
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Oumaima Hourrane
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Oumaima Hourrane
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Hagos Tesfahun Gebremichael
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Andiswa Bukula
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Rooweither Mabuya
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Rooweither /mabuya
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Salomey Osei
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Salomey Osei
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Abigail Oppong
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Abigail Oppong
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Tadesse Destaw Belay
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Tadesse Kebede Guge
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Tesfa Tegegne Asfaw
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Tesfa Tegegne Asfaw
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Chiamaka Ijeoma Chukwuneke
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Paul Röttger
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Paul Röttger
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Paul
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Seid Muhie Yimam
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Nedjma Ousidhoum
AfriHate: A Multilingual Collection of Hate Speech and Abusive Language
  Datasets for African Languages
Hate 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. Each instance in AfriHate is annotated by
native speakers familiar with the local culture. We report the challenges
related to the construction of the datasets and present various classification
baseline results with and without using LLMs. The datasets, individual
annotations, and hate speech and offensive language lexicons are available on
https://github.com/AfriHate/AfriHate
AfriHate, a multilingual dataset of hate speech in 15 African languages, addresses gaps in local moderation efforts, featuring native speaker annotations and baseline classification results including the use of large language models.
multilingual collection hate speech datasets native speakers large language models (LLMs)