用大模型生成对话式网络欺凌数据，更安全高效。
SynBullying: A Multi LLM Synthetic Conversational Dataset for Cyberbullying Detection
- 用多个大模型模拟真实多轮欺凌对话
- 包含上下文感知标注和细粒度分类标签
- 适合做欺凌检测模型训练或数据增强
我们提出 SynBullying，一个基于大语言模型（LLMs）生成的多模型对话式网络欺凌（CB）数据集。该数据集通过模拟真实欺凌交互，提供可扩展且伦理安全的替代方案，避免真人数据采集。其特点包括：（i）多轮对话结构，捕捉连续交流；（ii）上下文感知标注，结合语境、意图与话语动态评估伤害性；（iii）细粒度标签，覆盖多种欺凌类型，支持语言与行为分析。我们在五个维度上评估该数据集：对话结构、词汇模式、情感/毒性、角色动态、伤害强度及欺凌类型分布。进一步测试其作为独立训练数据或数据增强源在欺凌分类任务中的表现。
原文摘要 · Abstract (English)
We introduce SynBullying, a synthetic multi-LLM conversational dataset for studying and detecting cyberbullying (CB). SynBullying provides a scalable and ethically safe alternative to human data collection by leveraging large language models (LLMs) to simulate realistic bullying interactions. The dataset offers (i) conversational structure, capturing multi-turn exchanges rather than isolated posts; (ii) context-aware annotations, where harmfulness is assessed within the conversational flow considering context, intent, and discourse dynamics; and (iii) fine-grained labeling, covering various CB categories for detailed linguistic and behavioral analysis. We evaluate SynBullying across five dimensions, including conversational structure, lexical patterns, sentiment/toxicity, role dynamics, harm intensity, and CB-type distribution. We further examine its utility by testing its performance as standalone training data and as an augmentation source for CB classification.
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