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@article{201628,
        author = {Shaik.Mohammad Rafi and Prem.Battu and Shaik.Mastan Vali},
        title = {An AI-Driven Framework for Fake News Detection Using NLP and Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {4263-4266},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201628},
        abstract = {The rapid growth of digital media platforms has significantly increased the spread of fake news, creating serious social, political, and economic impacts. Detecting fake news manually is difficult due to the huge volume of online information generated every day. This paper proposes an AI-driven framework for fake news detection using Natural Language Processing (NLP) and Deep Learning techniques. The proposed system performs text preprocessing, feature extraction, and classification to identify whether a news article is fake or genuine. NLP techniques such as tokenization, stemming, stop-word removal, and word embedding are used to process textual data. Deep learning models including Long Short-Term Memory (LSTM) networks are utilized for effective classification. Experimental analysis demonstrates that the proposed framework achieves high accuracy and improves the reliability of automated fake news detection systems. The framework can be integrated into social media platforms and news applications to reduce the spread of misinformation.},
        keywords = {Fake News Detection, Natural Language Processing, Deep Learning, Artificial Intelligence, LSTM, Machine Learning.},
        month = {May},
        }