snap-stanford/humanual-opinion

---
license: cc-by-nc-4.0
language:
- en
tags:
- user-simulation
- humanlm
- persona
- social-media-and-opinion
pretty_name: Humanual-Opinion
size_categories:
- 10K<n<100K
---

# Humanual-Opinion

[](https://humanlm.stanford.edu)
[](https://humanlm.stanford.edu/HumanLM_paper.pdf)
[](https://github.com/zou-group/humanlm)
[](https://huggingface.co/collections/snap-stanford/humanual-datasets)

Reddit users expressing opinions across diverse personal-issue threads (r/AITA), reflecting moral standards on controversial topics like family conflicts and life decisions. This dataset is part of the **[HumanLM](https://humanlm.stanford.edu)** benchmark for training user simulators that accurately reflect real user behavior.

**Source:** Reddit r/AITA via asyncpraw · **Domain:** Social Media & Opinion · **Date Range:** 2018-11-12 to 2025-09-08

The dataset contains **42,332** comments from **4,567** users across **992** posts, with an average of **3.55** turns per conversation. Each example includes the user's persona, conversation context, and ground-truth response.

**Splits:** train (37,791) · val (1,177) · test (3,364)

| Column | Description |
|--------|-------------|
| `prompt` | Reddit post and parent comments as a list of messages with `role` and `content` fields |
| `completion` | The ground-truth user comment to generate |
| `persona` | User's commenting history and stance patterns on r/AITA |
| `post_id` | Reddit post ID |
| `user_id` | SHA-256 hashed Reddit username (for privacy) |
| `timestamp` | Unix timestamp of when the comment was posted |
| `turn_id` | Depth in the comment thread (1 = direct reply to post) |
| `metadata` | Reddit metadata as JSON (subreddit, score, awards, etc.) |

## Quick Start

```python
from datasets import load_dataset

dataset = load_dataset("snap-stanford/humanual-opinion")
sample = dataset["train"][0]

print(sample["persona"]) # User persona
print(sample["prompt"]) # Conversation context
print(sample["completion"]) # Ground-truth response
```

## Citation

```bibtex
@article{wu2026humanlm,
title={HUMANLM: Simulating Users with State Alignment Beats Response Imitation},
url={https://humanlm.stanford.edu/},
author={Wu, Shirley and Choi, Evelyn and Khatua, Arpandeep and Wang, Zhanghan and He-Yueya, Joy and Weerasooriya, Tharindu Cyril and Wei, Wei and Yang, Diyi and Leskovec, Jure and Zou, James},
year={2026}
}
```

Released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).