Machine learning approaches for detecting hate speech in African languages on social media: a systematic literature review

Banchale Adhi Gufu, Audrey Mbogho, Edward Ombui

Abstract


Significant research efforts has been made towards the development of machine learning models to detect hate speech worldwide. However, for Africa, which is home to over 2,000 languages with diverse linguistic dialects, there is an urgent need for inclusive natural language processing (NLP) tools tailored to the continent's linguistic diversity. More specifically, the literature review reveals that limited research has been conducted in hate speech detection in African languages, thus providing a strong justification for this study. Whereas hate speech has continued to intrigue African communities, detection has been hampered by the complexity of multiple languages, thus calling for a localised approach to solving the problem.

The study adopted the PRISMA guidelines for a systematic literature review (SLR) approach, synthesising findings from research published between 2019 and 2024 focusing on machine learning detection techniques in African low-resourced languages. The study contributes to the theoretical literature reviews and the development of Natural Language Processing (NLP) for African languages by providing a comprehensive review of research gaps in the machine learning models and datasets, highlighting the importance of multiple approaches and the need for collaborative, community-based measures to address the menace of hate speech perpetrated on social media. The findings reveal that machine learning models, including SVM, BiLSTM, mBERT, and XLM-RoBERTa, show significant potential in detecting hate speech in African languages. However, their performance is often constrained by the scarcity and limitations of available datasets. These findings provide valuable insights into the current state of hate speech detection for African languages and underscore the need to develop more comprehensive machine learning models and datasets for widely spoken African languages.

 

Received on, 30 July 2025

Accepted on, 18 October 2025

Published on, 26 November 2025


Keywords


Machine Learning; Hate Speech Detection; African Languages; Social Media; Natural Language Processing; Deep Learning; Text Classification; Multilingual NLP

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References


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DOI: https://dx.doi.org/10.21622/ACE.2025.05.2.1524

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Copyright (c) 2025 Banchale Adhi Gufu, Audrey Mbogho, Edward Ombui

Advances in Computing and Engineering

E-ISSN: 2735-5985

P-ISSN: 2735-5977

 

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