| --- |
| 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} |
| } |
| |
