Fake News Detection on Social Media using Machine Learning Techniques
Shivani Suresh Nikam1, Rupali Dalvi2

1Shivani Suresh Nikam*, Computer Engineering ,Savitribai Phule Pune University, India.
2Prof. Rupali Dalvi, Computer Engineering ,Savitribai Phule Pune University, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 30, 2020. | Manuscript published on May 10, 2020. | PP: 940-943 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5428059720/2020©BEIESP | DOI: 10.35940/ijitee.G5428.059720
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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: Web based life administrations, as Facebook and Twitter, Renren, Instagram, and linkedin have recently become an enormous and persistent supply of day by day news. These stages give a huge number of clients and give numerous administrations, for example, content arrangement and distributing. Not all distributed information via internet based medium is dependable and exact. Numerous individuals attempt to distribute fake and mistaken news so as to control general conclusion. Counterfeit news might be intentionally made to advance money related, political and public premiums, and can lead to unsafe effects on people convictions and choices.. In this paper we examine different systems for recognizing counterfeit information via internet based networking medium. Our point is to locate a dependable and right model that arranges a given article as fake or genuine. For identification of fake articles we use machine learning algorithms. 
Keywords: Fake news, Misinformation, Disinformation, Social media, Machine learning.