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@article{194414,
        author = {Dnyaneshwari kachare and Prof. N. L. Bagora and Sandip Anbhule and Soham Ghadge and Hrushi Jagtap},
        title = {Fake News Detection Using Machine Learning and Natural Language Processing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6452-6456},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194414},
        abstract = {The rapid spread of fake news on social media and digital platforms has become a major global concern. Misleading information can influence public opinion, create social unrest, and spread misinformation quickly. Traditional manual fact-checking methods are time-consuming and inefficient when dealing with large volumes of online content. This research proposes an automated Fake News Detection System using Machine Learning and Natural Language Processing techniques. The system preprocesses news articles, extracts textual features using TF-IDF vectorization, and applies classification algorithms such as Support Vector Machine (SVM) and Naïve Bayes to determine whether news content is real or fake. The model is trained on labeled datasets and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. Experimental results show that machine learning techniques can effectively classify news articles and help reduce the spread of misinformation. The proposed system can be integrated with news platforms and social media applications to provide real-time fake news detection and improve information reliability.},
        keywords = {},
        month = {March},
        }