Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{181691,
        author = {Gunjan Sayaji Patil and Prof. Vishnupant Potdar and Dr. Nagnath Biradar},
        title = {Fake News Detection Using Machine Learning and Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {5117-5121},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181691},
        abstract = {The unchecked proliferation of false information jeopardizes the credibility of content on the internet. In this study, we introduce a hybrid fake news detection system that integrates classical algorithms—such as Logistic Regression and XGBoost—with advanced neural networks like BiLSTM, a CNNBiLSTM architecture, and transformer-based models with a focus on RoBERTa. By leveraging multiple feature extraction strategies (including TF-IDF vectors and contextual embeddings) and combining outputs through ensemble techniques, our approach captures subtle semantic patterns and boosts overall classification performance. Tested on the Kaggle Fake and Real News Dataset, the ensemble consistently surpassed each standalone method, demonstrating robust generalization and multilingual capability. This flexible, high-throughput framework is well suited for realtime misinformation monitoring across various digital channels.},
        keywords = {Fake News, Natural Language Processing, RoBERTa, BiLSTM, Machine Learning, Deep Learning, Ensemble Learning},
        month = {June},
        }