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Open Access

ARTICLE

Deep-Learning Approaches to Text-Based Verification for Digital and Fake News Detection

Raed Alotaibi1,*, Muhammad Atta Othman Ahmed2, Omar Reyad3,4,*, Nahla Fathy Omran5
1 Applied College, Shaqra University, Shaqra, Saudi Arabia
2 Faculty of Computers and Information, Luxor University, Luxor, Egypt
3 College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia
4 Faculty of Computers and Artificial Intelligence, Sohag University, Sohag, Egypt
5 Faculty of Computers and Information, Qena University, Qena, Egypt
* Corresponding Author: Raed Alotaibi. Email: email; Omar Reyad. Email: email
(This article belongs to the Special Issue: Fake News Detection in the Era of Social Media and Generative AI)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.076156

Received 15 November 2025; Accepted 05 February 2026; Published online 27 February 2026

Abstract

The widespread use of social media has made assessing users’ tastes and preferences increasingly complex and important. At the same time, the rapid dissemination of misinformation on these platforms poses a critical challenge, driving significant efforts to develop effective detection methods. This study offers a comprehensive analysis leveraging advanced Machine Learning (ML) techniques to classify news articles as fake or true, contributing to discourse on media integrity and combating misinformation. The suggested method employed a diverse dataset encompassing a wide range of topics. The method evaluates the performance of five ML models: Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Decision Trees (DTs), and Support Vector Machines with Radial Basis Function (SVM-RBF) kernels. The presented methodology included thorough data preprocessing, detailed parameter tuning during model training, and robust statistical analyses to ensure fair and accurate performance comparisons. The results demonstrate that the combination of Term Frequency-Inverse Document Frequency (TF-IDF) with ANN and CNN achieved the highest accuracy of 99.13%, showcasing the effectiveness of these approaches in text-based news classification. The LSTM model followed closely with an accuracy of 98.59%, while the DT and SVM-RBF models achieved accuracies of 85.67% and 90.22%, respectively. These findings highlight the superior performance of deep learning (DL) models when combined with effective feature extraction techniques such as TF-IDF. The models offer practical utility and show promising potential for integration into editorial workflows to facilitate pre-publication news verification. Furthermore, statistical test methods such as Analysis of Variance (ANOVA) and Tukey’s Honestly Significant Difference (HSD) tests are also performed. The obtained results clarify significant performance differences among the evaluated models, highlighting their unique capabilities and comparative strengths in the context of fake news detection. Hence, the presented study reinforces the importance of artificial intelligence based tools in promoting media reliability and provides a foundation for future advancements in automated misinformation detection systems.

Keywords

Text verification; fake news detection; machine learning; deep learning; CNN; LSTM; news classification systems; misinformation detection

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