Update README.md

README.md

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+# 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
+
+---
+
+## 📜 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}
+}