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