Proactive Bot Detection Based on Structural Information Principles

Summary

Researchers propose SIAMD, a structural-information adversarial framework that models bot behavior and detects bots proactively. It organizes multi-relational interactions into a heterogeneous graph, uses structural entropy to find layered account communities, and uses large language models to generate synthetic messages that evolve the network. Experiments on real-world datasets report that SIAMD significantly and consistently outperforms state-of-the-art social bot detection baselines in effectiveness, generalizability, robustness, and interpretability.