HAUSA HATE SPEECH DETECTION USING LOGISTIC REGRESSION - Bashir, Idris Sulaiman; Muhammad, Aminu Ahmad (FUDMA Journal of Sciences, 2026)
Hate speech on social media poses serious risks to social cohesion, particularly in multilingual and politically sensitive regions such as West Africa. While Natural Language Processing techniques have achieved strong performance for high-resource languages, African languages remain under-represented due to limited annotated data and linguistic complexity. This study investigates hate speech detection in Hausa using traditional machine learning approaches, focusing on interpretability and efficiency in low-resource settings. Experiments are conducted on the AFRIHATE Hausa corpus using Logistic Regression as the primary classifier and Random Forest as a comparative model. Text is represented using Term Frequency-Inverse Document Frequency (TF-IDF) and Bag-of-Words features. Model performance is evaluated using accuracy, precision, recall, and F1-score under stratified cross-validation. Results show that Logistic Regression with TF-IDF features achieves the best overall performance, with an accuracy of 94% and an F1-score of 93%, outperforming Random Forest across feature representations. The findings indicate that simple, interpretable models remain strong baselines for Hausa hate speech detection and offer practical value for content moderation in low-resource African language contexts.