CROSS-PLATFORM IDENTIFICATION OF ANONYMOUS IDENTICAL USERS IN MULTIPLE SOCIAL MEDIA NETWORKS

Abstract

The project focuses on developing a cross-platform system to identify anonymous identical users across multiple social media networks. As users often create different profiles on various platforms to maintain anonymity, identifying these profiles as belonging to the same individual becomes a significant challenge. The system uses advanced data mining, machine learning algorithms, and pattern recognition to analyze user behavior, text patterns, and other metadata across multiple platforms. By linking these profiles, the project aims to help businesses, security experts, and researchers understand online behavior better, detect fraudulent activities, and provide personalized services without violating user privacy. The proposed solution enhances the ability to track identity consistency across different social media platforms while respecting privacy concerns.