Fake Social Media Accounts and Their Detection

Abstract

This project presents an AIbased fake social media account detection system using Machine Learning techniques to identify fake accounts accurately and efficiently. Fake accounts are widely used for spreading misinformation, online fraud, spam, phishing attacks, and identity theft, making their detection essential for maintaining the security of social networking platforms. The proposed system analyzes important profile features such as profile picture availability, username, biography, number of posts, followers, following count, account privacy status, and follower-to-following ratio to classify accounts as genuine or fake. The application is developed using Python, Flask, HTML, CSS, and JavaScript to provide a simple and user-friendly interface. Three Machine Learning algorithms— Random Forest, Decision Tree, and Logistic Regression—are implemented and compared to improve prediction accuracy. Among them, Random Forest achieves the best performance. The system provides fast, reliable, and automated detection with minimal human intervention, helping improve the safety, trustworthiness, and overall security of social media platforms.