Fake Social Media Profile Detection and Reporting

Abstract

The rapid growth of social media platforms has led to an increase in fake profiles that are used for spam, phishing, identity theft and spreading false information. Given the large number of users, such accounts are hard to detect manually. This paper proposes a Machine Learning-Based Fake Social Media Profile Detection and Reporting System for detecting fake accounts based on profile features such as followers, following, posts, biography, and verification status. The Random Forest algorithm is used to classify the profiles as genuine or fake for the proposed system. A reporting module for users to report suspicious accounts and an administrator dashboard to manage reported profiles. The system is developed using Python, Flask, HTML, CSS and JavaScript and it provides an accurate, secure and userfriendly solution to improve social media security. KEYWORDS: Machine Learning, Fake Social Media Profile Detection, Random Forest, Social Media Security, Profile Classification, Flask, Python, Cyber Security, Web Application.