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@article{180989,
        author = {Syed Zeeshaan and Mr. Mustafa Syed and Syed Abdul Razzaq Quadri and Syed Abdul Zubair},
        title = {Fake News Detection Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3230-3234},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180989},
        abstract = {Fake news refers to fabricated or false information presented as factual news, spreading confusion and misinformation among readers. Detecting fake news is critical in preserving societal trust and preventing misinformation's harmful effects. This project explores machine learning and natural language processing (NLP) techniques to classify news articles as "Real" or "Fake." Fake news has become a pervasive issue in the digital era, affecting public trust and shaping opinions based on misinformation. This study focuses on the design and implementation of a robust fake news detection system, emphasizing key aspects such as accuracy, scalability, and user reliability. Fake news refers to false or misleading information presented as factual, often created to influence opinions, spread propaganda, or generate revenue. The detection of fake news has become a critical area of research due to its widespread impact on society, including public trust, decision-making, and social harmony. This study explores methods to identify fake news using a combination of machine learning algorithms and natural language processing (NLP) techniques.},
        keywords = {Fake news detection, Machine learning, Natural language processing, Text classification},
        month = {June},
        }