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AUTOMATED DETECTION OF SWAHILI SCAM MESSAGES IN TANZANIA USING NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING MODELS

Domaine:

natural language processing

Type de record:

papermodeldataset
Créateur:
Gau
Éditeur:
POLISH JOURNAL OF SCIENCE
Hôte:avatar

In recent years, cybersecurity threats have become increasingly prevalent worldwide, with scam messages especially those delivered via text and online platforms emerging as a major concern. These messages aim to trick recipients into disclosing personal or financial information, leading to identity theft, privacy infringements, and financial loss. In Tanzania, the rapid growth of mobile communication technologies and mobile money services has intensified this threat. Kiswahili, being the dominant language in digital communication, adds a unique dimension to the problem. This study develops Natural Language Processing (NLP)-driven Machine Learning (ML) models to detect scam messages written in Kiswahili. A labeled dataset was constructed and models such as Support Vector Machine (SVM), Random Forest (RF), Bi-LSTM, and Swahili-BERT were evaluated. Results indicate that Swahili-BERT achieved the highest detection accuracy. The study highlights the importance of context-aware, language-specific NLP tools and recommends real-time, user-friendly, and privacy-compliant deployment strategies.

Visit

doi.org

Tasks

text classification

Languages

SwahiliSwahili, CoastalSwahili, Congo

Tags

Cybersecurity, Scam Detection, Kiswahili NLP, Machine Learning, Swahili-BERT, Mobile Money Fraud, Low-Resource Languages.

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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