Datasetpublic

snap-stanford/humanual-politics

Humanual-Politics Medium users responding to blog posts on political topics, featuring diverse political stances from users spanning different cultural backgrounds. This dataset is part of the HumanLM benchmark for training user simulators that accurately reflect real user behavior. Source: RapidAPI Medium endpoint · Domain: Long-form Content & Politics · Date Range: 2022-04-01 to 2025-11-04 The dataset contains 47,905 comments from 5,300 users across 14,724 posts, with an… See the full description on the dataset page: https://huggingface.co/datasets/snap-stanford/humanual-politics.

sourceHugging Facecc-by-nc-4.0updated 7mo agoView on Hugging Face

2likes362downloads

Dataset Card

Humanual-Politics

![Website](https://humanlm.stanford.edu) ![Paper](https://humanlm.stanford.edu/HumanLMpaper.pdf) [![GitHub](https://img.shields.io/badge/GitHub-Code-black)](https://github.com/zou-group/humanlm) [![Collection](https://img.shields.io/badge/HuggingFace-AllDatasets-yellow)](https://huggingface.co/collections/snap-stanford/humanual-datasets)

Medium users responding to blog posts on political topics, featuring diverse political stances from users spanning different cultural backgrounds. This dataset is part of the [HumanLM](https://humanlm.stanford.edu) benchmark for training user simulators that accurately reflect real user behavior.

Source: RapidAPI Medium endpoint · Domain: Long-form Content & Politics · Date Range: 2022-04-01 to 2025-11-04

The dataset contains 47,905 comments from 5,300 users across 14,724 posts, with an average of 1.73 turns per conversation. Each example includes the user's persona, conversation context, and ground-truth response.

Splits: train (45,429) · val (489) · test (1,987)

Column	Description
`prompt`	Medium article content as a list of messages with `role` and `content` fields
`completion`	The ground-truth user comment to generate
`persona`	User's commenting history and political stances on Medium
`post_id`	Medium article ID
`user_id`	SHA-256 hashed Medium user ID (for privacy)
`timestamp`	Unix timestamp of when the comment was posted
`turn_id`	Position in the comment thread
`metadata`	Article metadata as JSON (title, claps, author, tags, etc.)

Quick Start

python

from datasets import load_dataset

dataset = load_dataset("snap-stanford/humanual-politics")
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.