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---
license: mit
task_categories:
- text-classification
- token-classification
language:
- zh
- ja
- pt
- fr
- de
- ru
tags:
- toxicity
- hatespeech
pretty_name: Multi-Lingual Social Network Toxicity
size_categories:
- 100K<n<1M
---
# MLSNT: Multi-Lingual Social Network Toxicity Dataset
**MLSNT** is a multi-lingual dataset for toxicity detection created through a large language model-assisted label transfer pipeline. It enables efficient and scalable moderation across languages and platforms, and is built to support span-level and category-specific classification for toxic content.
This dataset is introduced in the following paper:
> **Unified Game Moderation: Soft-Prompting and LLM-Assisted Label Transfer for Resource-Efficient Toxicity Detection**
> 🏆 Accepted at **KDD 2025**, Applied Data Science Track
---
## 🧩 Overview
MLSNT harmonizes 15 publicly available toxicity datasets across **7 languages** using GPT-4o-mini to create consistent binary and fine-grained labels. It is suitable for both training and evaluating toxicity classifiers in multi-lingual, real-world moderation systems.
---
## 🌍 Supported Languages
- 🇫🇷 French (`fr`)
- 🇩🇪 German (`de`)
- 🇵🇹 Portuguese (`pt`)
- 🇷🇺 Russian (`ru`)
- 🇨🇳 Simplified Chinese (`zh-cn`)
- 🇹🇼 Traditional Chinese (`zh-tw`)
- 🇯🇵 Japanese (`ja`)
---
## 🏗️ Construction Method
1. **Source Datasets**
15 human-annotated datasets were gathered from `hatespeechdata.com` and peer-reviewed publications.
2. **LLM-Assisted Label Transfer**
GPT-4o-mini was prompted to re-annotate each instance into a unified label schema. Only examples where human and LLM annotations agreed were retained.
3. **Toxicity Categories**
Labels are fine-grained categories (e.g., `threat`, `hate`, `harassment`).
---
## 📊 Dataset Statistics
| Language | Total Samples | % Discarded | Toxic % (Processed) |
|----------------------|----------------|-------------|----------------------|
| German (HASOC, etc.) | ~13,800 | 28–69% | 32–56% |
| French (MLMA) | ~3,200 | 20% | 94% |
| Russian | ~14,300 | ~40% | 33–54% |
| Portuguese | ~21,000 | 20–44% | 26–50% |
| Japanese | ~2,000 | 10–25% | 17–45% |
| Chinese (Simplified) | ~34,000 | 29–46% | 48–61% |
| Chinese (Traditional)| ~65,000 | 37% | ~9% |
---
## 💾 Format
Each row in the dataset includes:
- `full_text`: The original utterance or message.
- `start_string_index`: A list of start string indices (start positions of toxic spans).
- `end_string_index`: A list of end string indices (end positions of toxic spans).
- `category_id`: A list of toxic category IDs (integer values).
- `final_label`: A list of toxic category names (string values).
- `min_category_id`: The minimum toxic category ID in the row (used as the primary label).
- `match_id`: A unique identifier composed of the original dataset name and a row-level ID.
---
## 🗂️ Category ID Mapping
| ID | Friendly Name |
|-----|---------------------------------------------|
| 0 | Non Toxic |
| 1 | Threats (Life Threatening) |
| 2 | Minor Endangerment |
| 3 | Threats (Non-Life Threatening) |
| 4 | Hate |
| 5 | Sexual Content / Harassment |
| 6 | Extremism |
| 7 | Insults |
| 8 | Controversial / Potentially Toxic Topic |
---
## 🔬 Applications
- Fine-tuning multi-lingual moderation systems
- Cross-lingual toxicity benchmarking
- Training span-level and category-specific toxicity detectors
- Studying LLM label transfer reliability and agreement filtering
---
## 🙏 Acknowledgments
We thank **Ubisoft La Forge**, **Ubisoft Montreal**, and the **Ubisoft Data Office** for their technical support and valuable feedback throughout this project.
This work was supported by **Ubisoft**, the **CIFAR AI Chair Program**, and the **Natural Sciences and Engineering Research Council of Canada (NSERC)**.
## 📜 Citation
If you use MLSNT in academic work, please cite:
```bibtex
@inproceedings{yang2025mlsnt,
title={Unified Game Moderation: Soft-Prompting and LLM-Assisted Label Transfer for Resource-Efficient Toxicity Detection},
author={Zachary Yang and Domenico Tullo and Reihaneh Rabbany},
booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
year={2025}
}
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