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All Journal Bulletin of Electrical Engineering and Informatics
Andrew Quansah
University of North Carolina
Electrical & Electronics Engineering

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Machine learning-based detection of fake news in Afan Oromo language Ayodeji Olalekan Salau; Kedir Lemma Arega; Ting Tin Tin; Andrew Quansah; Kwame Sefa-Boateng; Ismatul Jannat Chowdhury; Sepiribo Lucky Braide
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8016

Abstract

This paper presents a machine learning-based (ML) approach for identifying fake news on web-based social media networks. Data was acquired from Facebook to develop the model which was used to identify Afan Oromo's false news. The system architecture uses algorithms, such as support vector machines (SVM), k-nearest neighbor (KNN), and convolutional neural networks (CNNs) to detect and classify fake news. Existing models have limitations in understanding reported news accuracy compared with verified news. This study successfully resolved the challenges in the detection of social media fake news detection for the Afan Oromo language with the use of ML models and natural language processing (NLP) techniques. The results show that the SVM approach achieved a precision, recall, and F1-score, of 0.92, 0.92, and 0.90.
Co-Authors Ayodeji Olalekan Salau Ismatul Jannat Chowdhury Kedir Lemma Arega Kwame Sefa-Boateng Sepiribo Lucky Braide Ting Tin Tin