Detecting Fake Profile in Online Social Networks using EnsemStack Classification Algorithm

Online Social Media (OSN) Platforms are the most dominating in the world amongst all age groups. These platforms are termed to be dynamic because they have increasing users day by day. The main benefit of social networking over the internet is to connect with people and communicate better. Users are sharing their data, photos and personal information on OSN platform like Facebook, Instagram, LinkedIn, and Twitter. Criminals are also used OSN platforms for new attack methods such as fake identities, disinformation, and many more. According to a recent study, the number of accounts that exist on social networks far outweighs the number of users who actually use them. Hence the no. of fake profile on OSN platforms are increasing over the last decade. The proposed model based on EnsemStack classification mode has the potential to identify the OSN account as real or fake with more accuracy. Our model hence takes the input of the from Instagram user account from and extracts all the required features the account as real or fake. The primary objective of proposed system is to absolute the existence of these fake profiles in these OSNs using machine learning. Eliminating these fake profiles will help to curb all the malpractices.