Proactive Bot Detection Based on Structural Information Principles

Bot detection is crucial for combating misinformation and preserving the authenticity of online interactions on social media. However, the increasing sophistication of bots in mimicking genuine accounts and evading detection has created an ongoing arms race between detection systems and modeling techniques. In this paper, we propose a novel Structural Information principles-based Adversarial framework, namely SIAMD, designed to Model bot behaviors and achieve proactive Detection. This framework begins by organizing multi-relational interactions between user accounts and social messages into a unified heterogeneous structure, incorporating structural entropy to quantify the uncertainty inherent in historical activities. The high-dimensional entropy is then minimized to uncover a layered hierarchy within account communities, which facilitates activity determination and account selection in behavioral modeling for bot accounts. For each modeled bot and its selected account, SIAMD extracts historical messages and user descriptions to construct prompts and integrates large language models to generate the associated message content. By embedding synthetic message vertices and establishing multi-relational interactions within the original heterogeneous network, SIAMD achieves network evolution in both structure and content, thereby enhancing graph-based proactive detection in an adversarial manner. Extensive comparative experiments on well-established real-world datasets demonstrate that SIAMD significantly and consistently outperforms state-of-the-art detection baselines for social bots in terms of effectiveness, generalizability, robustness, and interpretability.