Online Threats Detection in Hausa Language

Abubakar Yakubu Zandam, Fatima Muhammad Adam, Isa Inuwa-Dutse

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


One of the widely used technological inventions is the Internet which gives rise to online social media platforms such as Twitter and Facebook to proliferate. These platforms are quite instrumental as a means for socialisation and information exchange among diverse users. The use of online social media to spread information can be both beneficial and harmful. From the positive side, the information can be useful in the areas of security, economy and climate change. Motivated by the growing number of online users and widespread availability of contents with the potential of causing harm, this study examines how online contents with threatening themes are being expressed in Hausa language. We collected the first collection of Hausa datasets with threatening contents from Twitter and develop a classification system to help in curtailing security risks by informing decisions on tackling insecurity and related challenges. We employ and train four machine learning algorithms: Random Forest (RF), XGBoost, Decision Tree (DT) and Naive Bayes, to classify the annotated dataset. The result of the classifications shows an accuracy score of 72% for XGBoost, 71% for RF, 67% for DT and Naive Bayes having the lowest of 57%.
Original languageEnglish
Title of host publication4th Workshop on African Natural Language Processing
Subtitle of host publicationAfricaNLP 2023
PublisherMasakhane Research Foundation
Number of pages11
Publication statusPublished - 1 May 2023
EventAfrican NLP in the Era of Large Language Models: AfricaNLP 2023 Workshop - Radisson Blu Hotel and Convention Center, Kigali, Rwanda
Duration: 5 May 20235 May 2023


WorkshopAfrican NLP in the Era of Large Language Models
Abbreviated titleAfricaNLP 2023
Internet address

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