Enhanced Social Media Profile Authenticity Detection Using Machine Learning Models and Artificial Neural Networks

Fake engagement is one of the main issues with online networks or ONSs, which are used to artificially boost an account's popularity, this study examines the effectiveness of seven sophisticated Machine Learning Algorithms, Random Forest Classifier, Decision Tree Classifier, XGBoost, LightGBM, Extra Trees Classifier, and SVM, and got 93% accuracy in Decision Tree Classifier. In order to solve overfitting issues and improve model resilience, the paper proposes Generative Adversarial Networks (GANs) and uses K-Fold Cross-Validation. Furthermore, design a Gan-ANN model that combines Batch Normalization and Artificial Neural Networks (ANN) with GAN-generated synthetic data is investigated. The enhanced dataset seeks to strengthen model performance and generalization when combined with cutting-edge modeling methods. This study aims to improve model scalability, predictive accuracy, and dependability across different machine learning paradigms.