In this thesis, we propose a hybrid method for detecting fake accounts on Instagram by using the
Binary Grey Wolf Optimization Algorithm (BGWO) and Particle Swarm Optimization (PSO). By
combining these two algorithms, we aim to leverage their complementary strengths and enhance
the overall optimization performance. We evaluate the proposed hybrid method by using four
classifiers, Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbor
(KNN), and Artificial Neural Network (ANN). The dataset used for the experiments contains
65329 Instagram accounts. We extract features from each account, including profile information,
posting behavior, and engagement metrics by feature selection using PSO. The results show that
the Hybrid optimization method (BGWOPSO) significantly outperformed both Binary Grey Wolf
Optimization (BGWO) and Particle Swarm Optimization (PSO) methods when using several
performance measures including accuracy, precision, recall, and AUC selecting the best optimal
features.
