Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Due to the ever-increasing usage of social media, it is now crucial to combat the dissemination of false information and decrease reliance on information retrieved from such sources. Since users' contacts with fake and unreliable news contribute to its spread on a personal level, social media platforms are continuously under pressure to come up with remedies that are successful. This propagation of false information has a negative impact on how people see a significant activity, thus it must be addressed in a contemporary manner. In order to construct multiple datasets for the true and fake news items, we collected 1356 news instances from different people via Twitter and news sources like PolitiFact for this study.In this study, a number of cutting-edge methods are contrasted, including convolutional neural networks (CNNs), ensemble methods, and attention processes. In LSTM bidirectional+ CNN network had 88.78% accurate with attentive mechanism, while 85% detection rate identify fake news by Ko et el.
Keywords:
bidirectional, CNN, LSTM, PolitiFact
Cite Article:
"A PROCESS FOR DETECTING FAKE NEWS USING DEEP LEARNING MODELS", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 7, page no.1286 - 1295, July-2022, Available :http://www.ijrti.org/papers/IJRTI2207202.pdf
Downloads:
000204844
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator