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@article{187531,
        author = {Dr. A .Prakash and Akshatha and Vasamsetti Prasanna and Varshini B R and Sushma K B},
        title = {Fake news detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {5456-5461},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187531},
        abstract = {The rapid growth of online media has increased the spread of fake news, leading to misinformation that affects public perception, social harmony, and decision-making processes. Manual verification processes lack scalability, exhibit inconsistent performance, and are unable to process the large and continuously increasing stream of online information. This study presents a Machine Learning-driven Fake News Detection System that uses TF-IDF text representation along with a trained classification model to identify misleading information. To support real-time user interaction, the model is integrated into a Flask-based web application connected to MongoDB, which provides secure login features, admin-approved user access, and storage of prediction results. The proposed work delivers an end-to-end solution covering model development, deployment, and evaluation, highlighting its effectiveness and suitability for practical use.},
        keywords = {Fake News, Machine Learning, TF-IDF, Flask, MongoDB, Classification, NLP.},
        month = {November},
        }