Social bot detection on Twitter: robustness evaluation and improvement

Abstract

Online social networks are easily exploited by social bots. Although the current models for detecting social bots show promising results, they mainly rely on Graph Neural Networks (GNNs), which have been proven to have vulnerabilities in robustness and these detection models likely have similar robustness vulnerabilities. Therefore, it is crucial to evaluate and improve their robustness. This paper proposes a robustness evaluation method: Attribute Random Iteration-Fast Gradient Sign Method (ARI-FGSM) and uses a simplified adversarial training to improve the robustness of social bot detection. Specifically, this study performs robustness evaluations of five bot detection models on two datasets under both black-box and white-box scenarios. The white-box experiments achieve a minimum attack success rate of 86.23%, while the black-box experiments achieve a minimum attack success rate of 45.86%. This shows that the social bot detection model is vulnerable to adversarial attacks. Moreover, after executing our robustness improvement method, the robustness of the detection model increased by up to 86.98%.