Fake News Detector

Rafe, Md. Lutfor Rahman; Nahreen, Mashiat; Abir, Rabiul Alam

dc.contributor.author	Rafe, Md. Lutfor Rahman
dc.contributor.author	Nahreen, Mashiat
dc.contributor.author	Abir, Rabiul Alam
dc.date.accessioned	2022-03-29T03:25:42Z
dc.date.available	2022-03-29T03:25:42Z
dc.date.issued	2021-03-30
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dc.identifier.uri	http://hdl.handle.net/123456789/1297
dc.description	Supervised by Mr. Md. Hamjajul Ashmafee, Lecturer, Department of Computer Science and Engineering(CSE) Islamic University of Technology(IUT), Board Bazar, Gazipur-1704. Bangladesh	en_US
dc.description.abstract	With the recent social media boom, the spread of fake news has become a great concern for everybody. It has been used to manipulate public opinions, influence the election - most notably the US Presidential Election of 2016, incite hatred and riots like the genocide of the Rohingya population. A 2018 MIT study found that fake news spreads six times faster on Twitter than real news. The credibility and trust in the news media are at an all-time low. It is becoming increasingly difficult to determine which news is real and which is fake. Various machine learning methods have been used to separate real news from fake ones. In this study, we tried to accomplish that using Passive Aggressive Classifier, LSTM and natural language processing. There are lots of machine learning models but these two have shown better progress. Now there is some confusion present in the authenticity of the correctness. But it definitely opens the window for further research. There are some of the aspects that has to be kept in mind considering the fact that fake news detection is not only a simple web interface but also a quite complex thing that includes a lot of backend work.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Fake News Detector	en_US
dc.type	Thesis	en_US