Abstract

This study introduces a novel hybrid methodology that integrates Particle Swarm Optimization (PSO) and Social Network Analysis (SNA) to enhance conversion prediction in Meta digital advertising campaigns. Real-world data was collected from four international Meta ad accounts promoting coaching programs, structured across three dimensions: demographic targeting (age, gender), platform placement, and daily performance metrics. Following an initial cleaning and balancing process, a baseline Random Forest classifier was trained, achieving an F1-score of 0.81. PSO was then employed to optimize hyperparameters, resulting in an improved F1-score of 0.83. In parallel, we applied SNA techniques to construct behavioral graphs based on feature similarity and centrality, generating new network-based predictors. Finally, we combined both optimized hyperparameters and SNA-derived features into a hybrid PSO-SNA model.