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Published Paper Details:
Paper Title
Fake News Detection Using Machine Learning
Authors
Ms.Shilpa N S, Janani J, M Manish Kumar, Mahesh D S, Sheshadri G M
Keywords
Fake News Detection, Machine Learning,Deep Learning Social Media Analysis
Abstract
The intentional spread of false or misleading information, commonly known as fake news, poses significant risks to individuals' perceptions and decision-making. Social media, as a dominant conduit for sharing information, has amplified the reach and impact of fake news, making efficient detection methods critical. This study examines and compares the effectiveness of several Machine Learning and Deep Learning approaches in detecting fake news using four distinct datasets. Our investigation reveals that the Random Forest algorithm combined with Bag of Words feature extraction delivers superior performance on the FARN Dataset, achieving an accuracy rate of 98.8%. Furthermore, we observe that TF-IDF consistently outperforms alternative feature extraction techniques, underscoring its value in fake news classification tasks.
IJCRT's Publication Details
Unique Identification Number - IJCRT25A1188
Paper ID - 296993
Page Number(s) - j169-j173
Pubished in - Volume 13 | Issue 11 | November 2025
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Ms.Shilpa N S, Janani J, M Manish Kumar, Mahesh D S, Sheshadri G M, "Fake News Detection Using Machine Learning", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 11, pp.j169-j173, November 2025, Available at :http://www.ijcrt.org/papers/IJCRT25A1188.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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