title: AfriHate: A Multilingual Collection of Hate Speech and Abusive Language
    Datasets for African Languages
summary: 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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paper_title: AfriHate: A Multilingual Collection of Hate Speech and Abusive Language
    Datasets for African Languages
paper_summary: 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