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Authors
Priyansu Sahoo,Tanmay Gautam,Pavan R A,Samrat Ganguli,Soundarya V
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Keywords
Fake News, Fake News Detection, Recurrent Neural Networks.
Abstract
Fake news refers to false or misleading information presented to as legitimate news. It is often produced to harm the reputation of individuals or organizations or to generate profit through advertising. While false information has been circulated throughout history, the term fake news originated in the 1890s when sensationalized newspaper reports were widespread. However, the term lacks a clear and consistent definition and is often broadly applied to any misleading content portrayed as news. Additionally, some high-profile figures have used the term to dismiss news that portrays them negatively. Moreover, fake news is typically defined by sensationalism, deceptive headlines, and a lack of reliable sources. Leveraging these features, researchers have developed machine learning models and algorithms for fake news detection. These models utilize textual analysis, linguistic patterns, and metadata to differentiate between authentic and false news articles.
The techniques employed for fake news detection, which encompass both supervised and unsupervised learning approaches. Supervised learning methods involve training classifiers on labelled datasets, while unsupervised techniques rely on clustering algorithms and anomaly detection to identify suspicious content. Additionally, deep learning architectures, including Recurrent Neural Networks (RNNs) and Transformers, have demonstrated effectiveness in capturing semantic nuances and contextual information, improving the accuracy of fake news detection systems.
IJCRT's Publication Details
Unique Identification Number - IJCRT25A3320
Paper ID - 280764
Author type - Indian Author
Page Number(s) - l502-l506
Pubished in - Volume 13 | Issue 3 | March 2025
DOI (Digital Object Identifier) -
No Of Downloads - 84
Author Country - India, 560076, Bangalore, Bangalore, 560076, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Priyansu Sahoo,Tanmay Gautam,Pavan R A,Samrat Ganguli,Soundarya V, "Real Time Fake News Detection Application", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 3, pp.l502-l506, March 2025, Available at :http://www.ijcrt.org/papers/IJCRT25A3320.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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