Fake News and Rumour Detection on Social Media
M.Uma Devi1, Harsh Tyagi2, Alokit Jindal3

1Mrs.M.Uma Devi, Department of CSE, SRMIST, Chennai, India.

2Harsh Tyagi, Department of CSE, SRMIST, Chennai, India.

3Alokit Jindal, Department of CSE, SRMIST, Chennai, India.

Manuscript received on 15 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 11 October 2019 | PP: 1172-1175 | Volume-8 Issue-11S September 2019 | Retrieval Number: K123609811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1236.09811S19

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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: PC Mediated Communication (CMC) advances like, for example, online journals, Twitter, Reddit, Facebook and other web based life presently have such a large number of dynamic clients that they have turned into an ideal stage for news conveyance on a mass scale. Such a mass scale news conveyance framework accompanies a proviso of faulty veracity. Building up the unwavering quality of data online is a strenuous and an overwhelming test yet it is basically essential particularly amid the time-touchy circumstances, for example, genuine crises which can have destructive impact on people and society. 2016 US Presidential race is an encapsulation of the previously mentioned crisis. In a study , it was concluded that the public’s engagement with phoney news through Facebook was higher than through standard sources. So as to battle the spread of malevolent and unplanned falsehood in online networking we built up a model to recognise counterfeit news. Counterfeit news recognition is a procedure of classifying news and estimating it on the continuum of veracity. Detection is done by classifying and clustering assertions made about the event followed by veracity assessment methods emerging from linguistic cue, characteristics of the people involved and network propagation dynamics..

Keywords: Fake News, Rumour Detection, Machine Learning, Twitter
Scope of the Article: Machine Learning