FAKE SOCIAL MEDIA ACCOUNT AND DETECTION

Abstract

Impersonation social media accounts have been a major issue, leading to disinformation, identity thefts, and cyber scams. Rule-based traditional techniques which have been employed for the identification of scam accounts have failed to work because they are unable to cope with the evolving fraud patterns. A machine learning-based Fake ID detection system has been presented to address the problem, utilizing a Random Forest Classifier to identify real or fake social media accounts. The process involves the evaluation of 11 significant features derived from user profiles, including username patterns, bio information, privacy settings, and account activity measures. A React frontend has been utilized to facilitate profile data entry, which is classified in real time by a Flask backend through RESTful APIs. The system implemented here attained an impressive accuracy of 91% and can be utilized as a powerful tool for the detection of spurious accounts. Future developments include the integration of image recognition with deep learning, cross-platform support, and the implementation of privacy-preserving techniques such as federated learning.