Detection of Fake Profiles on Social Networking

Abstract

The detection of fake profiles on social networking platforms is a pressing concern due to the proliferation of fraudulent accounts that undermine user trust and platform integrity. This paper proposes a novel framework for the automatic detection of fake profiles, leveraging the private information available within social networking platforms while respecting user privacy. The proposed scheme utilizes advanced algorithms and machine learning models to analyze various parameters, including user activity patterns, account creation details, and communication behavior, to identify potentially fraudulent accounts. Importantly, this approach ensures the preservation of user privacy by conducting analysis solely within the platform's closed environment without compromising sensitive personal information. Furthermore, the framework incorporates an alert system to notify platform administrators and users of suspicious activity indicative of fake identity creation, enabling proactive measures to prevent the spread of fake profiles and mitigate potential risks. Through the implementation of this framework, social networking companies can effectively combat the proliferation of fake profiles while upholding user privacy and fostering a safer and more trustworthy online environment for all users