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@article{180959,
        author = {Thanushree D S and Jamuna K S and chandana B S and Ruchitha M and Mrs.Deepthi C G},
        title = {Machine learning approaches for automated fake news detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3124-3129},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180959},
        abstract = {The proliferation of misinformation across digital media platforms has emerged as a critical societal challenge, necessitating robust automated detection mechanisms. This systematic review examines contemporary machine learning and natural language processing methodologies employed for identifying deceptive news content. Our analysis encompasses diverse computational approaches, ranging from conventional supervised learning paradigms to state of the-art deep neural architectures, evaluating their operational mechanisms, feature utilization patterns, and performance characteristics. We address fundamental obstacles including dataset limitations, bias considerations, adaptive misinformation strategies, and model interpretability concerns. Through comprehensive comparative analysis of existing methodologies, this review establishes the current state of research and identifies strategic directions for developing more effective and dependable fake news detection systems.},
        keywords = {},
        month = {June},
        }