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@article{151976,
        author = {Tanisha Arora and Ankita Agrawal and Neha Singh and Shivakankshi Singh and Nirupma Gupta and Shahida Khatoon},
        title = {Fake news detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {2},
        pages = {82-89},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151976},
        abstract = {This research paper thinks of the uses of NLP (Natural Language Processing) methods for recognizing the 'Fake news', that is, deceiving reports that comes from the non-respectable sources. Simply by building a model dependent on a tally vectorizer (utilizing word counts) or a (Term Frequency Inverse Document Frequency) tfidf framework, (word counts comparative with how regularly they're utilized in different articles in your dataset) can just get you up until this point. Be that as it may, these models don't consider the significant characteristics like word requesting and setting. It is entirely conceivable that two articles that are comparable in their promise include will be totally extraordinary in their significance. The information science local area has reacted by making moves against the issue. There is a contest called as the "Fake News Challenge" and Facebook is utilizing AI to sift counterfeit reports through of clients' channels. Combatting the phony news is an exemplary book arrangement project with a straight forward recommendation. Is it feasible for you to assemble a model that can separate between "Genuine "news and "Fake" news? So a proposed work on amassing a dataset of both Fake and genuine news and utilize a Naive Bayes classifier to make a model to characterize an article into fake or genuine dependent on its words and expressions.},
        keywords = {Naïve Bayes,Natural Language Processing (NLP),Real News, Fake News,Term Frequency Inverse Document Frequency (tfidf).},
        month = {},
        }