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@article{192552,
        author = {Ashy V Daniel and Abey A Shala and S V Harish and M Mobin Priyarson and C Sibin},
        title = {FAKE NEWS DETECTION USING LOGISTIC DETECTION AND TF-IDF VECTORIZATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {1864-1870},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192552},
        abstract = {This project seeks to tackle the spread of misinformation by creating a powerful automated system for detecting fake news. By utilizing machine learning techniques, the research concentrates on categorizing news articles as either "Real" or "Fake" based on their written content. The focus of this initiative is on applying TF-IDF (Term Frequency-Inverse Document Frequency) vectorization to convert raw text into numerical features, enabling the model to assess the significance of certain words within a dataset. The primary analytical emphasis is on utilizing Logistic Regression, a statistical approach selected for its effectiveness in tasks involving binary classification. This study investigates the connection between language patterns and the credibility of news, assessing the capability of a linear model to differentiate between misleading language and factual reporting. Instead of concentrating on external metadata, this research emphasizes the internal semantic organization of the text to offer a scalable method for real-time information verification.},
        keywords = {Machine Learning, TF-IDF, Logistic Regression, Binary Classification, Statistical Method},
        month = {February},
        }