An Efficient Supervised Method for Fake News Detection using Machine and Deep Learning Classifiers
Sruthi. M. S1, Rahul R2, Rishikesh G3
1Sruthi. M. S, Assistant Professor, Sri Krishna College of Technology, Department of Computer Science and Engineering, Coimbatore, (Tamil Nadu), India.
2Rahul R, UG student, Sri Krishna College of Technology, Department of Computer Science and Engineering, Coimbatore, (Tamil Nadu), India.
3Rishikesh G, UG student, Sri Krishna College of Technology, Department of Computer Science and Engineering, Coimbatore, (Tamil Nadu), India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3896-3899 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8930038620/2020©BEIESP | DOI: 10.35940/ijrte.F8930.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper comes up with the applications of Machine learning and deep learning algorithms for police work the ‘fake news’, that is, dishonorable news stories that come from the unauthorized article writers. This approach was enforced as software and tested against an information set. Aim is to separate the faux news, among the news spread in the articles. It’s required to create a model which is able to differentiate between “Real” news and “Fake” news. The model was created exploitation numerous deep and machine learning strategies. LSTM technique outperforms different classifiers and achieves the accuracy of 94%.
Keywords: Support Vector Machine, Natural Language Processing, classification, News.
Scope of the Article: Deep Learning.