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@article{208742,
        author = {Pratiksha Venkatesh Mane and Tanuja Jitendra Walunj and Ankita Ashok Bhole and Ashwini Sanjay Gagare},
        title = {A Machine Learning Approach for Fake News Detection Using NLP-Based Feature Extraction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {635-638},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208742},
        abstract = {The rapid growth of digital media and social networking platforms has made it easier for information to be created and distributed at a large scale. Although online platforms provide fast access to news, they also enable the rapid spread of fake news, misleading information, and fabricated content. Fake news can influence public opinion, create social unrest, damage reputations, and affect political, economic, and public-health decisions. Therefore, automated fake-news detection has become an important research area in Natural Language Processing (NLP) and machine learning. This paper presents a machine-learning approach using text preprocessing, TF-IDF representation, n-gram analysis, linguistic features, and supervised classification. Naive Bayes, Logistic Regression, Support Vector Machine, and Random Forest can be compared using accuracy, precision, recall, F1-score, and confusion matrices. The approach is efficient and comparatively interpretable, but its reliability depends on dataset quality, labeling, changing language patterns, and external verification.},
        keywords = {Fake News Detection; Machine Learning; Natural Language Processing; TF-IDF; Text Classification; Naive Bayes; Logistic Regression; Support Vector Machine; Responsible AI},
        month = {September},
        }