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@article{191936,
        author = {E Madhumitha and B Kalpana and B Shirisha and N Shwetha and T K Srujana and Dr. C V Madhusudan Reddy},
        title = {An Intelligent Fake News Identification System Using Deep Learning and NLP Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {8280-8283},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191936},
        abstract = {The rapid growth of digital media platforms has significantly increased the spread of fake news and misinformation, posing serious threats to public trust, social stability, and informed decision-making. Online news portals and social media enable information to propagate quickly, often without verification, making manual fact-checking ineffective and time-consuming. As a result, there is an urgent need for automated systems capable of identifying fake news with high accuracy and reliability.
This project presents a Fake News Detection System using Deep Learning and Natural Language Processing (NLP) that classifies news content based on textual analysis. The system preprocesses news articles using NLP techniques such as tokenization, stop-word removal, lemmatization, and feature extraction. Deep learning models including Long Short-Term Memory (LSTM) networks and transformer-based architectures are employed to learn semantic and contextual patterns in text. The system is trained and evaluated on benchmark fake news datasets, and performance is validated using accuracy, precision, recall, and F1-score metrics.
The proposed solution supports scalable deployment for journalism verification, media monitoring, and online content moderation. By integrating NLP preprocessing with deep learning-based classification, the system provides an effective and automated approach to mitigating the impact of misinformation in digital communication environments. 
Additionally, the system is designed to adapt to evolving misinformation patterns by learning from large and diverse datasets. The use of deep learning enables robust handling of complex linguistic structures, ambiguous statements, and context-dependent narratives commonly found in fake news articles. This approach reduces reliance on manual intervention and enhances the system’s applicability in real-time digital ecosystems. As a result, the proposed framework contributes to strengthening information credibility and promoting responsible content consumption across online platforms.},
        keywords = {Fake News Detection, Natural Language Processing, Deep Learning, Text Classification, Misinformation Analysis, Machine Learning, Information Security},
        month = {January},
        }