BotVA: Combating Social Bots via Variational Feature Augmentation and Adversarial Graph Learning

Abstract

Social bots threaten online platforms by spreading disinformation and manipulating public discourse. Graph neural networks have emerged as effective tools for bot detection by modeling user interactions, yet two fundamental challenges limit their practical deployment: severe class imbalance where bots constitute a small minority of users, and camouflaged edges where bots forge deceptive connections to humans to evade detection. Class imbalance causes decision boundaries to shift toward the majority class, while camouflaged edges corrupt neighborhood aggregation through spurious message passing. We present BotVA, a unified framework addressing both challenges through variational feature augmentation and adversarial graph learning. Our approach makes three key contributions: (i) a conditional variational autoencoder that models minority class distributions and synthesizes semantically coherent features, effectively expanding minority support in representation space; (ii) an adversarial training paradigm where a generator simulates camouflage by injecting deceptive edges while a graph transformer discriminator with semantic attention learns to identify and downweight such perturbations; and (iii) a two-stage training strategy that pretrains the variational module before alternating generator-discriminator optimization to ensure stable convergence. Experiments on three benchmarks demonstrate that BotVA achieves state-of-the-art accuracy and F1-score, exhibits strong robustness under camouflage perturbations, and maintains competitive performance with limited supervision.