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@article{195126,
        author = {Mrs.D. Kanakasatya and A.S.N. Bhagya Sri and Ch. Vani and K.S.R. Prasanna Kumar and B. Durga Rao},
        title = {Fake News Detection: Machine Learning and Natural Language Processing for Identifying Misinformation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8182-8187},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195126},
        abstract = {People around the world now get a lot of their news and information from social media and online news platforms. The fast sharing of information on these platforms has also caused a big increase in fake news, which is false or misleading info that pretends to be real news. Fake news spreading everywhere can cause real problems like people being misinformed, social unrest, and political manipulation. Traditional ways of spotting fake news mainly depend on journalists and experts checking things by hand, which takes a lot of time and can’t keep up with the huge amount of information being shared online.
This project aims to tackle the problem by creating an automated fake news detection system that uses Natural Language Processing and machine learning techniques. The system gets the news text ready by taking out stop words, punctuation, and any characters that don’t matter. After cleaning, the text is turned into numbers using the TF-IDF method. A Naive Bayes classifier is trained using these features to decide whether news articles are real or fake. The experimental results show that machine learning methods can detect fake news and help cut down on the spread of misinformation.},
        keywords = {Fake News Detection, Machine Learning, Natural Language Processing, Text Mining, Naive Bayes Classifier, TF-IDF Feature Extraction, News Classification, Misinformation Analysis, Data Preprocessing.},
        month = {March},
        }