ML-Powered Fake Account Detection

Social media fake identities pose a serious risk because they spread false information, encourage fraud, and violate users' privacy. This study suggests a strategy for identifying and resolving bogus profiles on social networking sites that is based on machine learning. The system automatically harvests user data, including interaction patterns, profile verification status, and follower/following counts, by using web scraping techniques powered by Selenium. This data is analyzed by sophisticated machine learning algorithms, such as the XGBoost classifier, which determine if a profile is real or fraudulent. Along with important elements like engagement measurements, the model incorporates social network analysis, behavioral analysis, and content filtering. It is trained using Grid Search CV, crossvalidation, and interpretability-enhancing SHAP values on labeled datasets. To guarantee accuracy and continual progress, systems for adaptive learning and real-time monitoring are also implemented. Experiments demonstrate good recall, accuracy, and precision. Because of its modular design, the system may be easily integrated as a standalone application or API, improving social media integrity by proactively reducing the dangers associated with bogus accounts.