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  Published Paper Details:

  Paper Title

FAKE NEWS DETECTION USING MACHINE LEARNING

  Authors

  Vishal singh,  Arun borale,  Hritik madke,  Mamta patil

  Keywords

News,machine learning,sequence matcher,difflib,instant scrapper

  Abstract


Most of the sensible phone usersvalue more highly to scan the news via social media over net. The news websites arcommercial enterprise the news and supplythe supply of authentication. The question is a way to attest the news and articles that arcirculated among social media like WhatsAppteams, Facebook Pages, Twitter andalternative small blogs & social networking sites. it's harmful for the society to believe on the rumors and faux to be a news. the requirement of AN hour is to prevent the rumors particularly within the developing countries like Asian nation, and concentrate on the right, echt news articles. This paper demonstrates a model and therefore themethodology for pretend news detection. Withthe assistance of Machine learning andlinguistic communication process, it's tried tomixture the news and later verify whether or not the news is real or pretend victimisationsupply regression. The results of the projectedmodel ar compared with existing models. Theprojected model is functioning well andshaping the correctness of results up toninetieth of accuracy.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2105728

  Paper ID - 207523

  Page Number(s) - g755-g759

  Pubished in - Volume 9 | Issue 5 | May 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Vishal singh,  Arun borale,  Hritik madke,  Mamta patil,   "FAKE NEWS DETECTION USING MACHINE LEARNING", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 5, pp.g755-g759, May 2021, Available at :http://www.ijcrt.org/papers/IJCRT2105728.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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