A Deep Learning-Based Approach for Detecting Afan Oromo Fake News on Social Media

Arega K.L.; Bagwari A.; Tune K.K.; Beyene A.M.; Rodriguez C.; Lezama P.; Salau A.O. · Mathematical Modelling of Engineering Problems (2025) · DOI: 10.18280/mmep.120930

Resumen

The research aims to develop a deep learning system for detecting fake news on social media using Afan Oromo news text. The study reviews related research, introduces phony news detection, and develops a methodology. The research explores linguistic challenges in the Afan Oromo language, focusing on identifying and categorizing misleading news content. It evaluates deep learning models and provides insights for stakeholders. The study empowers local communities to combat misinformation, considering cultural context and ethical considerations. It also addresses biases and biases that may affect accurate information dissemination. The pre-trained model is evaluated using metrics, and a system prototype is created. This research used a meticulously trained dataset to identify fake news in the Afan Oromo language. Convolutional neural networks (CNNs) were used to accurately categorize the data, with a feature set technique. The study found that convolutional networks achieved an impressive accuracy rate of 93%, surpassing traditional neural networks’ 92% accuracy. The proposed strategies achieved a 91% accuracy rate. The research suggests using deep learning to identify fake news on social media, highlighting the impact of false information on society. The approach involves scraping Afan Oromo posts, cleaning and filtering data, and consolidating and annotating the data.

Instituciones: Universidad Nacional Mayor de San Marcos