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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16919
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dc.contributor.authorNirban, Virendra Singh-
dc.contributor.authorShukla, Tanu-
dc.date.accessioned2025-01-24T10:56:13Z-
dc.date.available2025-01-24T10:56:13Z-
dc.date.issued2023-03-
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-031-27499-2_64-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16919-
dc.description.abstractThe recent proliferation of social media has undoubtedly brought about many benefits, but along with it also came a serious impediment to society in the form of “fake news” which has become an eminent barrier to journalism, freedom of expression, and democracy as a whole. The study aims to understand the currently used AI techniques for detecting fake news, identify their shortcomings, and compare them with the emerging models. We compared the performance of memory-based methods (LSTM and Bi-LSTM) with traditional methods. We also compared the changes in performance after applying ensemble learning approaches. The study aimed to identify suitable models for fake news detection. This is in hopes of eventually promoting a safe and healthy environment for sharing information and content online and, in the process, helping develop strategies and techniques to curb the spread of fake news on social media.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectHumanitiesen_US
dc.subjectAI techniquesen_US
dc.subjectLong short term memory (LSTM)en_US
dc.subjectSocial mediaen_US
dc.subjectFake newsen_US
dc.titleThe Role of AI in Combating Fake News and Misinformationen_US
dc.typeArticleen_US
Appears in Collections:Department of Humanities and Social Sciences

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