# SynBullying: A Multi LLM Synthetic Conversational Dataset for Cyberbullying Detection

arXiv: https://arxiv.org/abs/2511.11599 · HTML: https://mlsanity.com/paper/2511.11599/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [Generating Training Data with Language Models: Towards Zero-Shot Language Understanding](https://mlsanity.com/paper/2202.04538/) (2022)
- [GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation](https://mlsanity.com/paper/2104.08826/) (2021)
- [Not Enough Data? Deep Learning to the Rescue!](https://mlsanity.com/paper/1911.03118/) (2019)
- [Mean Birds: Detecting Aggression and Bullying on Twitter](https://mlsanity.com/paper/1702.06877/) (2017)
- [Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key](https://mlsanity.com/paper/2104.10788/) (2021)
- [Automatic Detection of Cyberbullying in Social Media Text](https://mlsanity.com/paper/1801.05617/) (2018)
- [ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information](https://mlsanity.com/paper/2409.14740/) (2024)
- [Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification](https://mlsanity.com/paper/2004.01820/) (2020)
- [Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity](https://mlsanity.com/paper/1910.11922/) (2019)
- [Synthetic vs. Gold: The Role of LLM Generated Labels and Data in Cyberbullying Detection](https://mlsanity.com/paper/2502.15860/) (2025)
- [A Multi-faceted Semi-Synthetic Dataset for Automated Cyberbullying Detection](https://mlsanity.com/paper/2402.10231/) (2024)
- [Generation of Probabilistic Synthetic Data for Serious Games: A Case Study on Cyberbullying](https://mlsanity.com/paper/2306.01365/) (2023)

## Built on by (most cited first, 1 of 1)

- [Do Social Patterns Hold in Synthetic Data? Analyzing Cyberbullying Dynamics in LLM-Generated and Authentic Dialogues](https://mlsanity.com/paper/2609.17549/) (2026)

## Abstract (arXiv)

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.