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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM58433

Fake News Detection: Fake News Detection Using NLP and Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM58433

Volume:

10 |

3 |

Mar 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM58433

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

ODP

Om D. Pawar1

RMS

Rishikesh M. Sonune2

AGP

Aditya G. Pawar3

TSC

Tejas S. Chavan4

PMHA

Prof. Munavvar H. Ali5

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Abstract

Most of the smart phone users prefer to read the news via social media over internet. The news websites are publishing the news and provide the source of authentication. The question is how to authenticate the news and articles which are circulated among social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs & social networking sites. It is harmful for the society to believe on the rumors and pretend to be a news. The need of an hour is to stop the rumors especially in the developing countries like India, and focus on the correct, authenticated news articles. This paper demonstrates a model and the methodology for fake news detection. With the help of Machine learning and natural language processing, it is tried to aggregate the news and later determine whether the news is real or fake using Support Vector Machine. The results of the proposed model is compared with existing models. The proposed model is working well and defining the correctness of results up to 93.6% of accuracy.

KEYWORDS

— Fake News Detection NLP Machine Learning BERT LSTM Text Classification.

Cite This Article

Om D. Pawar1, Rishikesh M. Sonune2, Aditya G. Pawar3, Tejas S. Chavan4, Prof. Munavvar H. Ali5, "Fake News Detection: Fake News Detection Using NLP and Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 3, Mar 2026. https://doi.org/10.55041/IJSREM58433

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Publication Date

3/29/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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