Identification of Fake vs. Real Identities on Social Media using Random Forest and Deep Convolutional Neural Network
Bharat Sampatrao Borkar1, Rajesh Purohit2
1Bharat Sampatrao Borkar, Research Scholar, Department of Computer Science & Engineering, School of Engineering & Technology, Suresh Gyan Vihar University, Jagatpura, Jaipur.
2Dr. Rajesh Purohit, Principal, School of Engineering & Technology, Suresh Gyan Vihar University, Jagatpura, Jaipur.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 7347-7351 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9739109119/2019©BEIESP| DOI: 10.35940/ijeat.A9739.109119
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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: Identity detection is very essential in social media platforms, various platform has facing fake accounts influence since couple of years in current eras. Many researchers has introduces approach for identify the fake profiles, but still system cant able to solve such issues. As these fake identities are being used by offenders for various malicious purposes, it has become necessity of time to identify them. The fake identities are categorized into two main types’ i.e. fake identities by bots and fake identities by humans. This system removes fake identities by bots during preprocessing and focuses mainly on identification of fake identities by humans as very little research has been made till now on the fake identities by humans. For classification we test for two different algorithms i.e. Random Forest (RF) and Recurrent Neural Network (RNN). The classification is based on various features such as user name, location, friends count, followers count and so on. Here, dataset used is that of Twitter.
Keywords: Social media; Identity deception; Cyber crimes; Machine learning; Random forest; Deep learning; Deep convolutional neural network; Activation functions.