Fake profile recognition using big data analytics in social media platforms

Abstract

Online social media platforms today have many more users than ever before. This increased fake profiles trends which is harming both social and business entities as fraudsters use images of people for creating new fake profiles. However, most of those proposed methods are out-dated and aren't accurate enough with an average accuracy of 83%. Our proposed solution, for this problem, is a Spark ML-based project that can predict fake profiles with higher accuracy than other present methods of profile recognition. Our project consists of Spark ML libraries including Random Forest Classifier and other plotting tools. We have described our proposed model diagram and tried to depict our results in graphical representations like confusion matrix, learning curve and ROC plot for better understanding. Research findings through this project illustrate that this proposed system has accuracy of 93% in finding fake profiles over social media platforms. While there is 7% false positive rate in which our system fails to correctly identify a fake profile.