A fri H ate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for A frican Languages (2025)

Abstract: 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 a tweet annotated by native speakers familiar with the regional culture. 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 performance in low-resource settings.

## introduction no one is born hating another person because of the color of his skin, or his background, or his religion. people must learn to hate, and if they can learn to hate, they can be taught to love, for love comes more naturally to the human heart than its opposite. - @xcite hate speech and abusive language are global phenomena that highly depend on specific sociocultural contexts. although they deviate from the norm on social media, hate speech and abusive language quickly attract significant attention, spread among online communities @xcite , and incite harm or violence on individuals in real life @xcite . tangible efforts to address these problems must take social and cultural contexts into account @xcite . however, in the absence of high-quality data or when excluding local voices from the collection and annotation processes, one may fail to build assistive tools that help address the problem and moderate such content. collecting hate speech and offensive language datasets is complex and time-consuming as researchers typically rely on keywords, hashtags, or user accounts to build datasets @xcite . they may need further insights from both moderators @xcite and affected communities @xcite . further, resources in languages other than english are scarce, especially for low-resource languages. to bridge this gap, we present afrihate a collection of new hate speech and abusive language twitter datasets in 15 languages spoken in various african regions: algerian arabic, amharic, igbo, kinyarwanda, hausa, moroccan arabic, nigerian pidgin, oromo, somali, swahili, tigrinya, twi, isixhosa, yorùbá, and isizulu. the datasets are annotated by native speakers and include three classes: hate, abusive/offensive, or neutral-neither hateful nor abusive. the targets of the hateful tweets were further labeled based on six common attributes used to discriminate against people: ethnicity, politics, gender, disability, religion, or other. we report the data collection and annotation strategies and challenges when building afri-hate, present various classification baselines with and without using llms, and discuss the results. we find that model performance highly depends on the language and that multilingual models can help boost the performance in low-resource settings. we publicly release the datasets and individual labels, in addition to manually-curated hate speech and offensive language lexicons. these provide a valuable foundation for the research community interested in hate speech and abusive language, african languages, and researchers interested in studying disagreements. ## related work the fast-spreading nature of hate speech and abusive language have been at the center of a significant amount of nlp work in recent years @xcite @xcite . however, as there is no unanimous definition of hate speech, researchers have adopted different ones when building resources. for instance, some studies define hate speech as any speech that can cause danger or harm to disadvantaged groups @xcite , others focus on whether the speech is intended to promote hatred @xcite , or whether it dehumanises protected groups @xcite , which leads to various challenges such as the lack of generalisability @xcite . despite africa being home to more than 2,000 languages and the increasing interest in building hate speech and offensive languages resources for non-english languages @xcite @xcite , few datasets focus on african languages, such as amharic @xcite @xcite , afaan oromo (ababu and woldeyohannis, 2022), yorùbá @xcite , hausa @xcite , and nigerian pidgin @xcite @xcite ; language code subregion spoken in script algerian arabic/darja arq north africa algeria arabic amharic amh east africa ethiopia, eritrea ethiopic hausa hau west africa northern nigeria, niger, ghana, and cameroon, latin igbo ibo west africa southeastern nigeria latin kinyarwanda kin east africa rwanda latin moroccan arabic/darija ary north africa morocco arabic/latin nigerian pidgin pcm west africa nigeria, ghana, cameroon, latin oromo orm east africa ethiopia, kenya, somalia latin somali som east africa somalia, ethiopia, djibouti, kenya latin swahili swa east africa kenya, tanzania, uganda, dr congo, rwanda, burundi, mozambique latin tigrinya tir east africa ethiopia, eritrea ethiopic twi twi west africa ghana latin xhosa tso southern africa mozambique, south africa, zimbabwe, eswatini latin yorùbá yor west africa southwestern and central nigeria, benin, and togo latin zulu zul southern afric southern africa latin ## conclusion we introduced afrihate, the first large-scale collection of hate and abusive language datasets in 15 african languages: algerian arabic, amharic, igbo, kinyarwanda, hausa, moroccan arabic, nigerian pidgin, oromo, somali, swahili, tigrinya, twi, xhoza, yorùbá, and zulu. the datasets were annotated by native speakers as hate speech, abusive, or neutral. we discussed our data collection strategies and highlighted the challenges faced during the data collection and annotation. we then reported baseline experiments using africacentric pre-trained language models as well as prompted open and closed llms showing a large gap in the performance across languages. afrihate is a first step towards building high-quality hate speech resources for african languages. we publicly release all the datasets, scripts, models, and lexicons to the research community.