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@article{200464,
        author = {Mansi Tyagi and Siddharth and Shubham Tyagi},
        title = {Fake News Detection Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1042-1044},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200464},
        abstract = {The rapid growth of online media platforms has significantly increased the spread of fake news, leading to misinformation and social instability. Detecting fake news manually is inefficient due to the vast volume of digital content generated daily. This research paper presents a machine learning-based approach for fake news detection using natural language processing techniques. The proposed system utilizes text preprocessing and TFIDF feature extraction, followed by supervised learning algorithms such as Naïve Bayes, Logistic Regression, Support Vector Machine, and Random Forest.
Experimental results demonstrate that the proposed approach achieves high accuracy and reliable classification performance, making it suitable for realworld applications.},
        keywords = {Fake News Detection, Machine Learning, Natural Language Processing, Text Classification, Social Media},
        month = {May},
        }