---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- Harmful
- toxic
- spam
- negative
size_categories:
- 1K<n<10K
---
# 🦣 Mastodon Wild Data for Harmful Content Detection

## Overview
The **Harmful Texts on Mastodon** dataset is a human-annotated corpus of **3,000 English posts** collected from the decentralized social media platform **Mastodon** between **December 2024 and February 2025**.  
It is designed to evaluate the **robustness**, **generalization**, and **personalization** capabilities of large language models (LLMs) and in-context learning (ICL) approaches for **harmful content detection in real-world scenarios**.

Unlike existing benchmark datasets, which are typically curated and balanced, this dataset captures the **natural distribution**, **domain shifts**, and **semantic overlaps** present in real-world social discourse.  
Each post is annotated at **three granularities** — *binary*, *multi-class*, and *multi-label* — allowing flexible evaluation under multiple task formulations.

---

## 📊 Dataset Structure

| Granularity | Labels | Description |
|--------------|---------|-------------|
| **Binary** | `benign`, `harmful` | Basic harmfulness classification. |
| **Multi-class** | `benign`, `toxic`, `spam`, `negative` | Mutually exclusive fine-grained categories. |
| **Multi-label** | One or more from `{benign, toxic, spam, negative}` | Allows overlapping or composite labels for nuanced real-world cases. |


## 🧠 Motivation

Existing datasets such as SST-2, TextDetox, and UCI SMS provide clean, well-curated benchmarks for harmful content detection.  
However, **real-world moderation** is far more complex — social media posts are **ambiguous**, **noisy**, and often contain **overlapping intents**.  
For example, a post can simultaneously express anger (*negative*) while using profanity (*toxic*) or contain excessive hashtags (*spam-like*) without malicious intent.

The **Mastodon Wild Data** dataset addresses these limitations by introducing a *“wild” benchmark* that captures the messiness and richness of real-world online discourse.  
It aims to:
- Evaluate **robustness and generalization** of large language models (LLMs) under domain shift.  
- Reflect the **compositional nature** of harmful content (e.g., *toxic + negative*).  
- Provide a unified resource for studying **multi-task**, **multi-class**, and **multi-label** formulations.  

---

## 🏗️ Data Construction

- **Source:** Public Mastodon posts (Dec 2024 – Feb 2025).  
- **Initial Corpus:** 8,998,738 posts → 3,948,831 unique English entries.  
- **Filtering Strategy:**
  1. Randomly sample **15,000** English posts.  
  2. Use **Llama-3 (48-shot Random)** ICL model for preliminary harmfulness prediction.  
  3. Select **1,500 predicted benign** and **1,500 predicted harmful** posts for manual annotation.  
- **Final Dataset:** **3,000 annotated posts**, balanced between harmful and benign examples.  
- **Annotation:** Each sample is labeled at three levels — binary, multi-class, and multi-label — by trained human annotators.

---

## 🧾 Label Statistics

### Multi-Class Distribution
| Label | Count | Percentage |
|--------|--------|-------------|
| Benign | 1798 | 59.9% |
| Negative | 755 | 25.2% |
| Toxic | 259 | 8.6% |
| Spam | 188 | 6.3% |

### Multi-label Label Distribution

| Labels | Count | Labels | Count | Labels | Count |
|:-------|------:|:-------|------:|:-------|------:|
| Benign | 1437 | Benign, Negative | 249 | Benign, Negative, Spam | 11 |
| Negative | 517 | Benign, Spam | 184 | Benign, Negative, Toxic | 13 |
| Spam | 60 | Benign, Toxic | 8 | Benign, Spam, Toxic | 5 |
| Toxic | 6 | Negative, Spam | 10 | Negative, Spam, Toxic | 3 |
| – | – | Negative, Toxic | 339 | – | – |
| – | – | Spam, Toxic | 98 | – | – |
| **Sum** | **2020** | – | **948** | – | **32** |


---

## 📚 Recommended Usage

This dataset is well-suited for:
- Evaluating **In-Context Learning (ICL)** and **prompt-based personalization** methods.  
- Studying **robustness** and **domain generalization** in harmful content detection.  
- Training or testing **multi-label** or **reason-augmented** classification frameworks.  
- Benchmarking **cross-task**, **multi-task**, and **multi-modal** content moderation models.

---

## ⚖️ License

The dataset is distributed under the **CC BY 4.0 License**.  
Users should also check the Terms of Service of the specific Mastodon instance you collect data from (e.g., [mastodon.social Terms of Service
](https://mstdn.social/terms-of-service)) when redistributing or reusing data derived from public posts.

---

## 🧩 Citation

If you use this dataset, please cite:

```bibtex  
@misc{zhang2025onesizefitsallpersonalizedharmfulcontent,
      title={Beyond One-Size-Fits-All: Personalized Harmful Content Detection with In-Context Learning}, 
      author={Rufan Zhang and Lin Zhang and Xianghang Mi},
      year={2025},
      eprint={2511.05532},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2511.05532}, 
}
```