Camer-Hate-FR: An Annotated Dataset for Hate Speech Detection in Cameroonian French.

This dataset, titled Camer-Hate-FR, provides a valuable resource for detecting hate speech within the unique linguistic context of Cameroonian French. The data consists of 46,825 messages collected between January and June 2025 from public Cameroonian social media sources, including Facebook pages, YouTube channels, and WhatsApp groups.

Existing hate speech detection models, primarily trained on standard European French, perform poorly on Cameroonian data due to the prevalent use of local slang, code-switching with English and indigenous languages (Camfranglais), and nuanced cultural contexts. This dataset was created to address this gap.

Each message has been manually annotated by three native speakers as either 'hateful' (haineux) or 'non-hateful' (non_haineux), with the final label determined by a majority vote. The dataset is provided as a single CSV file and includes the original text, the annotation counts, the final vote, and the justifications provided by annotators. All data has been fully anonymized to protect user privacy.

This resource is designed to train, validate, and benchmark machine learning models for content moderation, facilitate sociolinguistic analysis, and spur the development of more inclusive and effective NLP technologies for Francophone Africa.