Leveraging Machine Learning Techniques and NLP for Identifying Fake Accounts in Social Networks

The rise of malicious entities on social networking platforms has led to a significant increase in the creation and use of fake accounts, which can be exploited for misinformation, cybercrime, and social manipulation. To address this challenge, this project presents a machine learning-based approach for the detection of fake accounts using both structured metadata and natural language processing techniques. This project utilizes a comprehensive feature extraction process, considering username patterns, profile statistics, and textual metadata, followed by preprocessing and normalization. A comparative analysis of multiple classifiers - Random Forest, Support Vector Machine (SVM), Logistic Regression, and LightGBM - is conducted to evaluate detection accuracy. The models were trained on a labeled dataset and assessed on unseen test data using performance metrics including accuracy, precision, recall, and F1-score. Results indicate that the ensemble-based approaches outperform traditional models, with Random Forest and LightGBM achieving the highest accuracy. The solution is integrated into a userfriendly Streamlit application that allows real-time prediction and visual performance comparison of all models, making it suitable for non-technical users and potential deployment by platform administrators.