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

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.