Detecting coordinated inauthentic behavior on social media remains a critical and persistent challenge, as most existing approaches rely on superficial correlation analysis, employ static parameter settings, and demand extensive and labor-intensive manual annotation. To address these limitations systematically, we propose the Adaptive Causal Coordination Detection (ACCD) framework. ACCD adopts a three-stage, progressive architecture that leverages a memory-guided adaptive mechanism to dynamically learn and retain optimal detection configurations for diverse coordination scenarios. Specifically, in the first stage, ACCD introduces an adaptive Convergent Cross Mapping (CCM) technique to deeply identify genuine causal relationships between accounts. The second stage integrates active learning with uncertainty sampling within a semi-supervised classification scheme, significantly reducing the burden of manual labeling. The third stage deploys an automated validation module driven by historical detection experience, enabling self-verification and optimization of the detection outcomes. We conduct a comprehensive evaluation using real-world datasets, including the Twitter IRA dataset, Reddit coordination traces, and several widely-adopted bot detection benchmarks. Experimental results demonstrate that ACCD achieves an F1-score of 87.3\% in coordinated attack detection, representing a 15.2\% improvement over the strongest existing baseline. Furthermore, the system reduces manual annotation requirements by 68\% and achieves a 2.8x speedup in processing through hierarchical clustering optimization. In summary, ACCD provides a more accurate, efficient, and highly automated end-to-end solution for identifying coordinated behavior on social platforms, offering substantial practical value and promising potential for broad application.

翻译：检测社交媒体上的协同虚假行为仍是一项关键且持久的挑战，因为现有方法大多依赖浅层相关性分析、采用静态参数设置，并且需要大量劳动密集型的人工标注。为系统性地解决这些局限，我们提出了自适应因果协同检测（ACCD）框架。ACCD采用三阶段渐进式架构，利用记忆引导的自适应机制动态学习并保留针对不同协同场景的最优检测配置。具体而言，在第一阶段，ACCD引入自适应收敛交叉映射（CCM）技术，以深度识别账户间真实的因果关系。第二阶段在半监督分类方案中集成主动学习与不确定性采样，显著减轻了人工标注的负担。第三阶段部署由历史检测经验驱动的自动化验证模块，实现对检测结果的自我验证与优化。我们使用真实世界数据集进行了全面评估，包括Twitter IRA数据集、Reddit协同行为轨迹以及多个广泛采用的机器人检测基准。实验结果表明，ACCD在协同攻击检测中取得了87.3%的F1分数，相比现有最强基线提升了15.2%。此外，该系统通过层次聚类优化将人工标注需求降低了68%，处理速度提升了2.8倍。总之，ACCD为识别社交平台上的协同行为提供了一种更精准、高效且高度自动化的端到端解决方案，具有重要的实用价值和广阔的应用前景。