Fake News Detection Using Machine learning and Models

Author(s)	KHATRI GAURAVKUMAR KALPESHKUMAR
Country	India
Abstract	People are using social media more and more in place of conventional news sources because of the widespread use of social media and cellphones. Although news websites offer verified sources, false information and fake news frequently proliferate unchecked on social media sites like“Facebook, Twitter, WhatsApp, and other microblogging platforms. This unchecked spread of misleading information has the potential to mislead the public and cause needless fear. In this work, an artificial intelligence-based model for Natural Language Processing (NLP)-based false news detection is presented. The Logistic Regression (LR) algorithm is used in the suggested approach to aggregate news information and assess its validity. When compared to other false news detection methods, the model's performance shows that it is successful at differentiating between fake and real news, with an accuracy of 98.6%.”
Keywords	Fake News Detection, Machine Learning (ML), Natural Language Processing (NLP), Fake News Dataset, Real vs. Fake News, Logistic Regression
Field	Computer Applications
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-02-21
Cite This	Fake News Detection Using Machine learning and Models - KHATRI GAURAVKUMAR KALPESHKUMAR - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.37439
DOI	https://doi.org/10.36948/ijfmr.2025.v07i01.37439
Short DOI	https://doi.org/g85sx7

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