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@article{165600,
        author = {Jahnvi Srivastava and Dr. Pooja Khanna},
        title = {A MACHINE LEARNING APPROACH TO DETECT FAKE NEWS ARTICLES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {1},
        pages = {1721-1726},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165600},
        abstract = {In an era dominated by digital information dissemination, the proliferation of fake news has emerged as a formidable challenge. This paper presents a comprehensive overview of techniques and methodologies employed in the detection of fake news. Delving into various dimensions including textual, visual, and social cues, it sheds light on the intricacies and complexities of the detection process. Through a critical evaluation of state-of-the-art algorithms using benchmark datasets, we assess their strengths and limitations. This study illuminates the current landscape of fake news detection and provides valuable insights for future research in this critical domain. As misinformation continues to threaten the credibility of information sources, our endeavors in fake news detection stand as a beacon of hope in upholding the integrity of information and fortifying public trust.},
        keywords = {Algorithms, Fake News, Machine Learning, Performance Evaluation},
        month = {June},
        }