---
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}
}