Analysis on Fake News Detection Methodologies
Vaishnavi R1, Anitha Kumari S2

1Vaishnavi R, Department of Computer Science and Engineering, LBS Institute of Technology for Women, Trivandrum, India.
2Anitha Kumari S, Department of Computer Science and Engineering, LBS Institute of Technology for Women, Trivandrum, India.

Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 1572-1575 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2448059120/2020©BEIESP | DOI: 10.35940/ijrte.A2448.059120
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Abstract: Fake news is a coinage often used to refer to fabricated news that uses eye-catching headlines for increased sales rather than legitimate well-researched news, spread via online social media. Emergence of fake news has been increased with the immense use of online news media and social media. Low cost, easy access and rapid dissemination of information lead people to consume news from social media. Since the spread rate of these contents are faster it becomes difficult to identify the fake news from the accurate information. People can download articles from sites, share the content, re-share from others and by the end of the day the false information has gone far from its original site that it becomes very difficult to compare with the real news. It is a long standing problem that affects the digital social media due to its serious threats of misleading information, it creates an immense impact on the society. Hence the identification of such news are relevant and so certain measures needs to be taken in order to reduce or distinguish between the real and fake news. This paper provides a survey on recent past research papers done on this domain and provides an idea on different techniques on machine learning and deep learning that could help in the identification of fake and real news. 
Keywords: Deep Learning, Fake News, Machine Learning Natural Language Processing.
Scope of the Article: Deep Learning