A novel Quantum Beta distributed multi-objective Particle Swarm Optimization algorithm for fake accounts detection

Detecting fake accounts on Online Social Networks is a pressing issue due to the rise in unethical online activities. This study presents a new Quantum Beta-behaved Multi-Objective Particle Swarm Optimization Algorithm (QB-MOPSO) for machine learning-based fake account detection. QB-MOPSO aims to enhance the learning process of a random forest algorithm by simultaneously minimizing feature dimensionality and classification error rates. It proposes a novel architecture that employs two optimization profiles: one improves exploratory behavior using a quantum-behaved equation, while the other enhances exploitation through a beta function. The main contributions of this study are as follows: the design of a novel Quantum Beta Distributed Multi-Objective Particle Swarm Optimization algorithm that integrates quantum-behaved exploration and beta-distributed exploitation, the application of this algorithm to enhance artificial intelligence–based fake account detection on Twitter datasets, and a comprehensive experimental evaluation demonstrating superior accuracy, F-measure, and MCC compared to existing methods. Experimental results on two Twitter datasets with 1982 and 928 accounts respectively show QB-MOPSO's effectiveness, achieving accuracy rates of about 99.19 % and 97.52 %. Comparisons with the original architecture demonstrate QB-MOPSO's ability to enhance the performance of the random forest algorithm.