Fake Social Media Profile Detection and Reporting

ABSTRACT : The rapid growth of social media platforms has led to an increase in the creation and misuse of fake profiles, posing significant threats to user security, online trust, and information integrity. Fake profiles—ranging from bots and impersonators to sockpuppets—are often used for malicious activities such as spreading misinformation, executing scams, and manipulating public opinion. This article explores the various methods employed to detect fake accounts, including machine learning algorithms, natural language processing (NLP), image analysis, and social network graph theory. It also addresses the major challenges involved in fake profile detection, such as evolving attacker strategies, data privacy limitations, and scalability issues. Furthermore, the article outlines best practices for users and highlights emerging technologies that promise more accurate and real-time detection. The study emphasizes the need for a multi-layered, adaptive approach to combat the growing sophistication of fake profiles on social media.