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COVID-19 Fake News Classification using Deep Learning

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dc.contributor.advisor Dailey, Prof. Matthew N.
dc.contributor.author Koirala, Abhishek
dc.contributor.other Dung, Prof. Phan Ming
dc.contributor.other Silpasuwanchai, Dr. Chaklam
dc.date.accessioned 2020-08-13T04:15:18Z
dc.date.available 2020-08-13T04:15:18Z
dc.date.issued 2020
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/981
dc.description.abstract False and deceptive news has been around for a very long time. False stories have been used to fame or defame people, destroying real facts and events, creating propaganda, and causing biases among people to create social, and economic disturbance. However, only in recent times have people fully understood the far-reaching impact of deceptive news. The impact of fake news on the 2016 US presidential election, and BREXIT has been widely discussed by many researchers. Researchers have thus turned towards finding the best approaches to detect and classify fake news in real time before it goes to the general public. Researchers generally focus on one particular domain at a time, because natural language is complex system and generalizing over multiple domains to obtain a common approach is not the best way to begin with. I chose COVID-19 as my domain because in the early days of the pandemic, there were a large number of false stories circulating among the public creating chaos and even discrimination in society. This research study discusses various approaches to fake news classification in the COVID-19 new domain using machine learning and deep learning. en_US
dc.language.iso en_US en_US
dc.subject COVID-19 en_US
dc.subject NLP en_US
dc.subject NLU en_US
dc.subject fake news en_US
dc.subject machine learning en_US
dc.subject deep learning en_US
dc.title COVID-19 Fake News Classification using Deep Learning en_US
dc.type Research report en_US


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