Copyright © 2025 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{188178,
        author = {Nandani Sahu and Shanu Gour},
        title = {Review on Fake News Detection Using Natural Language Processing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {589-599},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188178},
        abstract = {The unprecedented growth of digital media and social networking platforms has accelerated the spread of misinformation, posing significant threats to societal trust, public health, political stability, and democratic processes. Traditional manual fact-checking mechanisms are insufficient to counter the volume, speed, and sophistication of fake news circulation. This study presents a comprehensive analysis of Natural Language Processing (NLP)-based approaches for automated fake news detection, synthesizing contemporary machine-learning, deep-learning, and hybrid multimodal methods. Through a detailed examination of linguistic patterns, transformer-based architectures, stance analysis, knowledge-augmented models, and graph-based frameworks, the research identifies key features and performance parameters critical to effective misinformation detection. Findings highlight that hybrid systems integrating textual semantics, contextual metadata, social-behavioral signals, and adversarial defense strategies outperform text-only models. The study further emphasizes the importance of multilingual datasets, cross-domain adaptability, explainability, and ethical AI deployment. This research contributes to strengthening digital information integrity and provides a foundation for developing scalable, transparent, and robust NLP-driven fake news detection systems.},
        keywords = {Fake News Detection, Natural Language Processing (NLP), Machine Learning, Deep Learning},
        month = {December},
        }