• Priyanka Shahane
• Deipali Gore

Identity deception on different social media platforms is increasing rapidly with huge growth of these platforms. As these fake identities are being used by criminals for different malicious purposes, it has become necessity of time to identify them. The fake identities are categorized into two main types i.e. fake identities produced by bots and fake identities produced humans . This system removes fake identities produced by bots during preprocessing and focuses mainly on identification of fake identities produced by humans as very less research has been made till now on the fake identities produced by humans. For classification we test for two different algorithms i.e. Random Forest (RF) and Deep Convolutional Neural Network (DCNN). 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.

social media; identity deception; cyber crimes; machine learning; random forest; deep learning; deep convolutional neural network; activation functions

"Identification of Fake vs. Real Identities on Social Media using Random Forest and Deep Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.82-89, June 2019, Available :http://www.jetir.org/papers/JETIR1907E12.pdf

"Identification of Fake vs. Real Identities on Social Media using Random Forest and Deep Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp82-89, June 2019, Available at : http://www.jetir.org/papers/JETIR1907E12.pdf