---
license: cc-by-4.0
task_categories:
- text-classification
language:
- zh
tags:
- fact-checking
- misinformation
size_categories:
- 1K<n<10K
---

# TrendFact

TrendFact is the Chinese fact-checking benchmark from our ACL 2026 paper *TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking*.

Unlike earlier datasets built mostly on Wikipedia, most claims in TrendFact are drawn from trending platforms, so every sample comes with real dissemination signals. We use it to study a mostly overlooked question: when claims differ in social impact, should — and can — a fact-checking system adapt how much reasoning effort it spends on them? We call this the Hotspot Perception Ability (HPA).

## Data

- **7,643** samples across five domains: public health, science, society, politics, and culture
- an evidence library of **366,634** entries
- supports three tasks: evidence retrieval, fact verification, and explanation generation

### `TrendFact.json`

The main data. Each sample has the following fields:

| Field | Description |
|-------|-------------|
| `claim` | the claim to be verified |
| `label` | 0 = SUPPORT, 1 = REFUTE, 2 = NEI (not enough info) |
| `evidence` | gold evidence, with `text` / `url` / `target_idx` |
| `explanation` | human-annotated explanation |
| `domain` | domain the claim belongs to |
| `views` / `Discussion` / `Engagemen` / `Post` | four hotspot indicators |
| `influence_score` / `final_influence_score` | influence scores |
| `ori_title` / `url` / `date` | original title, source link, and date of the claim |

### `evidence.parquet`

The evidence library, 366,634 entries in total, with fields `url` / `text` / `publish_date`. Used for the evidence retrieval task.

## Citation

```bibtex
@inproceedings{zhang2026trendfact,
  title={TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking},
  author={Zhang, Xiaocheng and Wang, Xi and Lu, Yifei and Wang, Jianing and Ye, Zhuangzhuang and Bao, Mengjiao and Yan, Peng and Su, Xiaohong},
  booktitle={Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={26494--26513},
  year={2026}
}
```

Code: https://github.com/zxc123cc/TrendFact