Fake Profile Identification in Online Social Networks Using Machine Learning

Abstract

Social networking platforms are now a common aspect of daily life for most people. Every day, a large number of people create profiles on social networking sites and interact with others, regardless of their location or time of day. Social networking platforms not only benefit users, but also put their security and personal information at danger. To find out who is spreading hazards on social media, we must classify user profiles. The classification allows us to distinguish between legitimate profiles on social networks and fake profiles. We generally employ a range of methods for categorising fraudulent profiles on social networks. As a result, we must improve the social network phoney profile identification system's accuracy rate. In this research, we propose machine learning and natural language processing (NLP) approaches for fraudulent profile detection. Both the Nave Bayes algorithm and the Support Vector Machine (SVM) can be employed.