# Synthetic vs. Gold: The Role of LLM Generated Labels and Data in Cyberbullying Detection

arXiv: https://arxiv.org/abs/2502.15860 · HTML: https://mlsanity.com/paper/2502.15860/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [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)
- [NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes](https://mlsanity.com/paper/2310.15959/) (2023)
- [Generate, Annotate, and Learn: NLP with Synthetic Text](https://mlsanity.com/paper/2106.06168/) (2021)
- [ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information](https://mlsanity.com/paper/2409.14740/) (2024)
- [A Multi-faceted Semi-Synthetic Dataset for Automated Cyberbullying Detection](https://mlsanity.com/paper/2402.10231/) (2024)

## Built on by (most cited first, 4 of 4)

- [SynBullying: A Multi LLM Synthetic Conversational Dataset for Cyberbullying Detection](https://mlsanity.com/paper/2511.11599/) (2025)
- [Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Models](https://mlsanity.com/paper/2505.02858/) (2025)
- [ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links](https://mlsanity.com/paper/2509.01387/) (2025)
- [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)

Cyberbullying (CB) presents a pressing threat, especially to children, underscoring the urgent need for robust detection systems to ensure online safety. While large-scale datasets on online abuse exist, there remains a significant gap in labeled data that specifically reflects the language and communication styles used by children. The acquisition of such data from vulnerable populations, such as children, is challenging due to ethical, legal and technical barriers. Moreover, the creation of these datasets relies heavily on human annotation, which not only strains resources but also raises significant concerns due to annotators exposure to harmful content. In this paper, we address these challenges by leveraging Large Language Models (LLMs) to generate synthetic data and labels. Our experiments demonstrate that synthetic data enables BERT-based CB classifiers to achieve performance close to that of those trained on fully authentic datasets (75.8% vs. 81.5% accuracy). Additionally, LLMs can effectively label authentic yet unlabeled data, allowing BERT classifiers to attain a comparable performance level (79.1% vs. 81.5% accuracy). These results highlight the potential of LLMs as a scalable, ethical, and cost-effective solution for generating data for CB detection.