# AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages

arXiv: https://arxiv.org/abs/2501.08284 · HTML: https://mlsanity.com/paper/2501.08284/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 61 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 27 of 27)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://mlsanity.com/paper/2012.15761/) (2020)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://mlsanity.com/paper/2309.07445/) (2023)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [Multilingual and Multi-Aspect Hate Speech Analysis](https://mlsanity.com/paper/1908.11049/) (2019)
- [Aya 23: Open Weight Releases to Further Multilingual Progress](https://mlsanity.com/paper/2405.15032/) (2024)
- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://mlsanity.com/paper/2302.08956/) (2023)
- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)
- [Spread of hate speech in online social media](https://mlsanity.com/paper/1812.01693/) (2018)
- [Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models](https://mlsanity.com/paper/2206.09917/) (2022)
- [Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go](https://mlsanity.com/paper/2103.00153/) (2021)
- [Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments](https://mlsanity.com/paper/2203.11764/) (2022)
- [The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who](https://mlsanity.com/paper/2304.14238/) (2023)
- [InkubaLM: A small language model for low-resource African languages](https://mlsanity.com/paper/2408.17024/) (2024)
- [Correcting FLORES Evaluation Dataset for Four African Languages](https://mlsanity.com/paper/2409.00626/) (2024)
- [HERDPhobia: A Dataset for Hate Speech against Fulani in Nigeria](https://mlsanity.com/paper/2211.15262/) (2022)
- [NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data](https://mlsanity.com/paper/2403.19260/) (2024)
- [EkoHate: Abusive Language and Hate Speech Detection for Code-switched Political Discussions on Nigerian Twitter](https://mlsanity.com/paper/2404.18180/) (2024)
- [Exploring Boundaries and Intensities in Offensive and Hate Speech: Unveiling the Complex Spectrum of Social Media Discourse](https://mlsanity.com/paper/2404.12042/) (2024)

## Built on by (most cited first, 11 of 11)

- [AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text](https://mlsanity.com/paper/2503.18247/) (2025)
- [HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing](https://mlsanity.com/paper/2505.14311/) (2025)
- [Natural Language Processing for Tigrinya: Current State and Future Directions](https://mlsanity.com/paper/2507.17974/) (2025)
- [Afri-MCQA: Multimodal Cultural Question Answering for African Languages](https://mlsanity.com/paper/2601.05699/) (2026)
- [Exploring Cultural Nuances in Emotion Perception Across 15 African Languages](https://mlsanity.com/paper/2503.19642/) (2025)
- [A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings](https://mlsanity.com/paper/2505.12116/) (2025)
- [SAFARI: A Community-Engaged Approach and Dataset of Stereotype Resources in the Sub-Saharan African Context](https://mlsanity.com/paper/2602.22404/) (2026)
- [Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South](https://mlsanity.com/paper/2605.19190/) (2026)
- [Beyond Majority Voting: Agreement-Based Clustering to Model Annotator Perspectives in Subjective NLP Tasks](https://mlsanity.com/paper/2605.09955/) (2026)
- [Language-Specific Gaps in AI Safety Training Datasets](https://mlsanity.com/paper/2608.13695/) (2026)
- [GENDIS: Gender-Related Discourse and Stance in Multilingual YouTube Comments from India, Kenya, and Nigeria](https://mlsanity.com/paper/2610.17778/) (2026)

## Abstract (arXiv)

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