In the digital era, effective identification and analysis of verbal attacks are essential for maintaining online civility and ensuring social security. However, existing research is limited by insufficient modeling of conversational structure and contextual dependency, particularly in Chinese social media where implicit attacks are prevalent. Current attack detection studies often emphasize general semantic understanding while overlooking user response relationships, hindering the identification of implicit and context-dependent attacks. To address these challenges, we present the novel "Hierarchical Attack Comment Detection" dataset and propose a divide-and-conquer, fine-grained framework for verbal attack recognition based on spatiotemporal information. The proposed dataset explicitly encodes hierarchical reply structures and chronological order, capturing complex interaction patterns in multi-turn discussions. Building on this dataset, the framework decomposes attack detection into hierarchical subtasks, where specialized lightweight models handle explicit detection, implicit intent inference, and target identification under constrained context. Extensive experiments on the proposed dataset and benchmark intention detection datasets show that smaller models using our framework significantly outperform larger monolithic models relying on parameter scaling, demonstrating the effectiveness of structured task decomposition.

翻译：在数字时代，有效识别与分析言语攻击对维护网络文明和保障社会安全至关重要。然而，现有研究受限于对对话结构和上下文依赖的建模不足，尤其是在隐性攻击普遍存在的中文社交媒体中。当前的攻击检测研究往往强调通用的语义理解，而忽视了用户回应关系，这阻碍了对隐性和上下文相关攻击的识别。为应对这些挑战，我们提出了新颖的"分层攻击评论检测"数据集，并基于时空信息提出了一种用于言语攻击识别的分治式细粒度框架。该数据集显式编码了分层回复结构和时间顺序，捕捉了多轮讨论中复杂的交互模式。基于此数据集，该框架将攻击检测分解为分层子任务，其中专门的轻量级模型在受限上下文中处理显式检测、隐性意图推理和目标识别。在提出的数据集和基准意图检测数据集上进行的大量实验表明，采用我们框架的较小模型显著优于依赖参数扩展的较大单体模型，证明了结构化任务分解的有效性。