Machine learning-based detection of fake news in Afan Oromo language

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.