CSC：LLM时代面向冲突感知社交机器人检测的校准简洁性
CSC: Calibrated Simplicity for Conflict-Aware Social Bot Detection in the LLM Era
浏览论文内容
中文总结 AI 辅助
针对LLM时代社交机器人检测中的模态冲突问题，提出CSC框架，通过简化图专家、校准融合和轻量级不一致性专家，在多个基准上提升校准决策质量并保持竞争力。
中文摘要 AI 辅助
社交机器人检测对于保护在线平台免受虚假信息放大、协调操纵和扭曲的公共话语至关重要。然而，大型语言模型使得仅从文本中检测社交机器人变得困难得多，因为语义伪装现在成本低廉、流畅且可扩展。由此产生的挑战是模态冲突：一个账户可能在语义上看起来像人类，而在图结构、个人资料属性或跨模态一致性方面仍显可疑。最近的基于图的检测器通过增加图侧复杂性来解决这一局限性，例如稀疏原型选择、自适应门控或特定于架构的控制逻辑，然而我们的实验表明，仅靠复杂性并不是解决此类冲突的最可靠方式。因此，我们提出了CSC，一个用于冲突感知的LLM时代社交机器人检测的校准简洁性框架。该框架结合了三个设计选择：一个简化的原型引导图专家，保留有用的结构偏差同时移除不稳定的图侧启发式方法；校准的单纯形约束融合，在后期融合前对齐异构置信空间；以及一个轻量级不一致性专家，用于建模跨模态分歧。在TwiBot-22、TwiBot-20和MGStBot-large上的实验表明，CSC提高了校准工作点决策质量，同时在外部基准上保持竞争力。进一步的分析表明，校准提高了置信度可靠性，不一致性专家主要在高冲突或接近阈值的区域提供局部修正，而简化的图侧控制产生了更好的稳定性-成本权衡。一项针对性的语义伪装压力测试进一步表明，在平衡的挑战集上，用匹配的人类文本替换选定的机器人文本会急剧降低独立文本专家的性能，而图和融合证据保持稳定。
英文摘要
Social bot detection is essential for protecting online platforms from misinformation amplification, coordinated manipulation, and distorted public discourse. However, large language models have made social bots much harder to detect from text alone because semantic camouflage is now cheap, fluent, and scalable. The resulting challenge is modality conflict: an account may look human-like in semantics while remaining suspicious in graph structure, profile attributes, or cross-modal consistency. Recent graph-based detectors tackle this limitation by adding graph-side complexity, such as sparse prototype selection, adaptive gating, or architecture-specific control logic, yet our experiments suggest that complexity alone is not the most reliable way to resolve such conflict. We therefore propose CSC, a calibrated-simplicity framework for conflict-aware LLM-era social bot detection. The framework combines three design choices: a simplified prototype-guided graph expert that retains useful structural biases while removing unstable graph-side heuristics, calibrated simplex-constrained fusion that aligns heterogeneous confidence spaces before late fusion, and a lightweight inconsistency expert that models cross-modal disagreement. Experiments on TwiBot-22, TwiBot-20, and MGStBot-large show that \textsc{CSC} improves calibrated operating-point decision quality while remaining competitive across external benchmarks. Further analyses show that calibration improves confidence reliability, the inconsistency expert mainly provides localized corrections in high-conflict or near-threshold regions, and simplified graph-side control yields a better stability-cost trade-off. A targeted semantic-camouflage stress test further shows that replacing selected bot text with matched human text sharply degrades the standalone text expert while leaving graph and fused evidence stable on a balanced challenge set.
发表机构
- Minzu University of China（中央民族大学）
- City University of Macau（澳门城市大学）
机构由 AI 辅助整理，请以论文原文为准。
