Fake Social Media Accounts and Their Detection

Abstract: Social media platforms allow users to communicate, share information and build online communities. Along with genuine users, however, platforms may contain fake accounts created with false identities, copied profiles, misleading information, automated activity or the intention to perform spam and other harmful activities. Detecting such accounts is therefore an important problem in social-media security and trust management. This project presents a conceptual machine-learning based approach for identifying suspicious social-media accounts using profile, activity, network and content-related features. The proposed workflow collects account information, preprocesses the data, extracts features, trains a classification model and produces a prediction such as genuine or suspicious/fake. Common machine learning methods such as Logistic Regression, Random Forest and Support Vector Machine can be evaluated using accuracy, precision, recall and F1-score. The project also considers challenges such as changing user behaviour, class imbalance, privacy and adversarial behaviour. The objective is to develop an understandable and scalable framework that can support early identification of potentially fake accounts while keeping human review in the decision process.

Keywords: Fake Account, Social Media, Bot Detection, Machine Learning, Feature Extraction, Classification.