Fake User Detection in Twitter using Random forest algorithm with Python
Sai Poojitha Bommadevara1, A. Jitendra2, S. Babu3

1Sai Poojitha Bommadevara*, CSE, V R Siddhartha Engineering College, Vijayawada, India. Email:
2A.Jitendra, CSE, V R Siddhartha Engineering College, Vijayawada, India. Email:
3S.Babu CSE, V R Siddhartha Engineering College, Vijayawada, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on May 01, 2020. | Manuscript published on May 10, 2020. | PP: 1293-1296 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5919059720/2020©BEIESP | DOI: 10.35940/ijitee.G5919.059720
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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: Millions of users are engaged with social networking sites around the world. Social sites like twitter, Facebook have a large impact on rare unwanted consequences caused in our regular life in user’s interactions. In order to disperse a large amount of inappropriate and harmful data protruding social networking sites are made as a target platform for the spammers. Twitter is main example that has become one of the important platforms for unreasonable amount of spam in all the tomes for fake users to tweet and promote websites or services that crates a major effect for legitimate users and also it disturbs resource consumption. By resulting the opening for unusual and harmful information there is an increase of fake identities that expands invalid data. Research on current online social networks (OSN) is quite natural for detection of fake users on twitter. In this paper using random forest classifier and ROC curve to detect fake users. 
Keywords:  Classifier, Detection, Fake user, Twitter.
Scope of the Article: Classification