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arXiv · 2511.11599

SynBullying: A Multi LLM Synthetic Conversational Dataset for Cyberbullying Detection

Arefeh Kazemi·Hamza Qadeer·Joachim Wagner·Hossein Hosseini·Sri Balaaji Natarajan Kalaivendan·Brian Davis
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Abstract

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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Arefeh Kazemi, Hamza Qadeer, Joachim Wagner, Hossein Hosseini, Sri Balaaji Natarajan Kalaivendan, Brian Davis. 2025-10-30. SynBullying: A Multi LLM Synthetic Conversational Dataset for Cyberbullying Detection. https://arxiv.org/abs/2511.11599

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