Detecting Fake Accounts with Multi-Platform ML
Velde Deepika, Erugurala Jyothsna, Shivunuri Snigdha Priya, Kiran Mannem, Tavanam Venkata Rao, S.N. Chandra Shekhar · 2025
Maintaining user trust and digital security is severely hampered by the growing number of fake accounts on social media sites. This paper aims to improve platform integrity by creating a reliable system that can identify fake accounts using advanced machine learning techniques. Traditional methods for detecting such accounts have relied heavily on heuristic-based strategies. These methods lack the scalability and adaptability required to cope with evolving fraudulent activities. The proposed Detecting Fake Accounts with Multi-Platform ML (DFMML) framework overcomes existing limitations by leveraging diverse datasets from Instagram, Facebook, and X (formerly Twitter) to train multiple machine learning models, including AdaBoost, Bagging, Decision Trees, Logistic Regression, Random Forest, and Gradient Boosting. A user-friendly Streamlit application has also been built, offering real-time detection results with dedicated pages for each platform.