Fake Social Media Profile Detection And Reporting

Abstract

  The proliferation of fraudulent profiles on social media platforms poses significant threats to user security, trust, and overall platform integrity. This research proposes a machine learning-based framework for the detection of fake social media profiles by leveraging a wide range of features extracted from user content, behavioral patterns, and network structures. A labeled dataset comprising genuine and fake profiles is utilized to train and evaluate the system. In addition to detection, the framework incorporates a reporting mechanism that allows users to flag suspicious accounts, thereby supporting the proactive removal of malicious entities. The system's performance is assessed using standard classification metrics such as accuracy, precision, recall, and F1-score. Furthermore, the effectiveness of the proposed model is validated through deployment on a real-world social media environment to demonstrate its practical utility in reducing the spread of fraudulent accounts and enhancing user experience.