Research article
● Open access
Fake News Detection Using Machine Learning: A Comprehensive Review of Techniques, Comparative Analysis, and a Novel Hybrid Ensemble Framework
Abstract
The rapid spread of misinformation on social media poses significant threats to democratic processes, public health, and social stability. Automated fake news detection using machine learning has become essential to support fact-checkers and platform moderation. This study presents a systematic comparative analysis of machine learning-based fake news detection approaches published between 2020 and 2025, focusing on classical, hybrid, and deep learning methods. Classical classifiers such as Naïve Bayes, Support Vector Machines, and Decision Trees achieve moderate accuracies (70–86%) but are limited by shallow feature representation and sensitivity to class imbalance. Hybrid and deep learning approaches improve performance (88–91%) but introduce higher computational complexity and resource requirements.
Building on this analysis, we propose a hybrid ensemble framework combining Logistic Regression, Random Forest, and XGBoost with TF-IDF feature extraction and Synthetic Minority Oversampling Technique (SMOTE). Experimental evaluation on the FakeNewsNet dataset demonstrates superior performance, achieving 96.96% accuracy, 96.9% F1-score, and an AUC of 0.994. Cross-validation confirms robustness; however, cross-domain testing reveals reduced generalizability (78.3% accuracy), and adversarial evaluation highlights vulnerability to text manipulation. Computational costs are higher than single models, and interpretability decreases due to ensemble complexity.
The findings demonstrate that carefully designed ensemble methods can substantially outperform individual classifiers, but challenges related to domain adaptation, adversarial robustness, computational efficiency, and explainability remain critical for real-world deployment. The study provides practical guidance for developing balanced, deployable fake news detection systems and outlines future research directions in cross-domain generalization, model compression, and multimodal integration.
Keywords
Fake news detection, misinformation, machine learning, ensemble learning, hybrid classification, Logistic Regression, Random Forest, XGBoost, natural language processing, text classification, TF-IDF, SMOTE, class imbalance, model interpretability, SHAP, cross-domain generalization, adversarial robustness, social media analysis
References
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Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151. https://doi.org/10.1126/science.aap9559
Al-Tarawneh, M. A. B., Al-Khresheh, A., Al-Irr, O., Kulaglic, A., Danach, K., Kanj, H., et al. (2025). Towards accurate fake news detection: Evaluating ensemble and classical methods. European Journal of Pure and Applied Mathematics, 18(2), 6087. https://doi.org/10.29020/nybg.ejpam.v18i2.6087
Dev, D. G., & Bhatnagar, V. (2024). Hybrid RFSVM: Hybridization of SVM and random forest models for detection of fake news. Algorithms, 17(10), 459. https://doi.org/10.3390/a17100459
Hamed, S. K., Ab Aziz, M. J., & Yaakub, M. R. (2023). A review of fake news detection approaches: A critical analysis of relevant studies and highlighting key challenges associated with the dataset, feature representation, and data fusion. Heliyon, 9(10), e20382. https://doi.org/10.1016/j.heliyon.2023.e20382
Hoy, N., & Koulouri, T. (2025). An exploration of features to improve the generalisability of fake news detection models. Expert Systems with Applications, 275, 126949. https://doi.org/10.1016/j.eswa.2025.126949
Ilyas, M. A., Rehman, A., Abbas, A., Kim, D., Naseem, M. T., & Min Allah, N. (2024). Fake news detection on social media using ensemble classifiers. Computers, Materials and Continua, 81(3), 4525-4549. https://doi.org/10.32604/cmc.2024.056291
Janssen, J. (2023). Comparative analysis of machine learning algorithms for fake news detection. Unpublished manuscript.
Lakshmi, V. D., & Kumari, C. S. (2022). Detection of fake news using machine learning models. International Journal of Computer Applications, 183(47), 22-27. https://doi.org/10.5120/ijca2022921874
Mishra, A., Khan, M. H., Khan, W., Khan, M. Z., & Srivastava, N. K. (2022). A comparative study on data mining approach using machine learning techniques: Prediction perspective. In Pervasive Healthcare (pp. 153-165). Springer.
Parveen, N., & Khan, M. W. (2024). Proposed algorithm and models for sentiment analysis and opinion mining using web data. Nanotechnology Perceptions, 20(6), 3900-3910.
Saini, P., & Khatarkar, V. (2023). A review on fake news detection using machine learning. Smart Moves Journal IJOScience. https://doi.org/10.24113/ijoscience.v9i2.511
SK, S., Allada, V. R., Asif, M., Sabeel, U. R., & Shariq, M. B. (2024). Fake news detection using deep learning (LSTM). International Journal of Creative Research Thoughts, 12(5), 2320-2882.
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151. https://doi.org/10.1126/science.aap9559