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Sani, Nura Muhammad
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Computer Science & IT

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Journal : Scientific Journal of Computer Science

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Hybrid Deep Learning Model for Fake News Detection on Social Media Using CNN-GRU on X formerly known as Twitter Muhammad, Lawan Jibril; Mohammed, Isa Umar; Sani, Nura Muhammad
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.400

Abstract

The spread of fake news on social media platforms has created a dilemma for the world community by spreading false information and eroding public confidence. Fake news spreads quickly and seriously harms society. Predicting and identifying fake news is crucial for preserving the integrity of information ecosystems in the wake of an epidemic of multiple high-profile disinformation efforts. In order to detect fake news, this work suggests a hybrid deep learning algorithm called Convolutional Neural Network - Gated Recurrent Unit (CNN-GRU), which combines the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) learning algorithms in an efficient manner. Models for identifying fake news were developed using deep learning-based methods, such as CNN, GRU, and CNN-GRU deep learning algorithms. Four standard performance metrics—accuracy, precision, recall, and F1-score—were used to evaluate the models. Nevertheless, the CNN-GRU deep learning-based detection model outperformed models created with CNN and GRU, achieving the maximum accuracy of 98.77%, 98.68%, 98.73%, and 98.71% for precision, recall, and F1-score, respectively. With a combined accuracy of 98.77%, precision of 98.68%, recall of 98.73%, and F1-score of 98.71%, the CNN-GRU deep learning-based false news detection model performs better than the two other deep learning-based models.
Co-Authors Mohammed, Isa Umar Muhammad, Lawan Jibril