---
title: "TrendFact — paper by Zhang et al. (2026) — summary and ideas"
description: "The proposed FactISR framework raises HPA and reduces compute. 25 references. Among them: 0."
authors: ["Xiaocheng Zhang", "Xi Wang", "Yifei Lu", "Jianing Wang", "Zhuangzhuang Ye", "Mengjiao Bao", "Peng Yan", "Xiaohong Su"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1219/"
doi: "10.18653/v1/2026.acl-long.1219"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2026-trendfact-benchmark-hotspot-perception-automatic/"
---
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# TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking

**Authors:** Xiaocheng Zhang, [Xi Wang](https://md.research.elamur.ai/free/en/authors/xi-wang/), Yifei Lu, [Jianing Wang](https://md.research.elamur.ai/free/en/authors/jianing-wang/), Zhuangzhuang Ye, [Mengjiao Bao](https://md.research.elamur.ai/free/en/authors/mengjiao-bao/), Peng Yan and Xiaohong Su · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1219/) · [PDF](https://aclanthology.org/2026.acl-long.1219.pdf) · [DOI 10.18653/v1/2026.acl-long.1219](https://doi.org/10.18653/v1/2026.acl-long.1219) · [OpenAlex](https://openalex.org/W7166858723)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Fact-checking systems built on LLMs and reasoning models (RLMs) usually spend the same effort on every claim. The authors argue this is risky. Claims that spread widely turn out to be harder to verify, and getting them wrong can cause social harm up to roughly 20 times greater. They call the missing skill Hotspot Perception Ability (HPA): spending more reasoning and evidence on high-impact claims and less on minor ones. No existing benchmark could measure it.

TrendFact fills this gap. It is a Chinese benchmark that draws claims from trending platforms and the CHEF dataset. Each claim comes with gold evidence, a human-written explanation, and popularity metadata, which together yield an influence score.

The authors add two metrics: ECS for explanation reliability and HCPI for overall HPA. Current fact-checking methods and LLM/RLM systems perform poorly on the benchmark. The proposed FactISR framework raises HPA and reduces compute. This makes TrendFact a practical testbed for impact-aware, resource-limited fact-checking.

## Key points
- **Dataset size and scope:** TrendFact has 7,643 samples and an evidence library of 366,634 entries. It spans five domains (public health, science, society, politics, culture) and supports three tasks: evidence retrieval, claim verification, and explanation generation.
- **Influence score:** each claim's score combines four social signals (views, discussions, engagements, posts) with a GPT-assessed risk score.
- **Metrics:** ECS measures whether explanations are consistent with the reasoning behind the verdict. HCPI combines ECS with the influence score to quantify HPA.
- **FactISR framework:** it pairs Dynamic Evidence Augmentation (DEA), which retrieves evidence iteratively as needed, with influence-driven self-reflection (ISR), which adjusts reasoning depth. On QwQ-32B and Qwen3-32B, it raises HCPI by up to almost 4%.
- **DEA efficiency:** DEA keeps accuracy steady while cutting reasoning time by up to 30% and reasoning length by up to 50%. Code and data are available at github.com/zxc123cc/TrendFact.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) — University, Beijing, China · 415 works · 94,028 citations
- [Harbin Institute of Technology](https://md.research.elamur.ai/free/en/universities/harbin-institute-of-technology/) — University, Harbin, China · 185 works · 29,140 citations
- [Beihang University](https://md.research.elamur.ai/free/en/universities/beihang-university/) — University, Beijing, China · 86 works · 9,097 citations
- [Northeastern University](https://md.research.elamur.ai/free/en/universities/northeastern-university/) — University, Boston, United States · 68 works · 7,316 citations
- [East China Normal University](https://md.research.elamur.ai/free/en/universities/east-china-normal-university/) — University, Shanghai, China · 69 works · 2,490 citations
- [National University of Defense Technology](https://md.research.elamur.ai/free/en/universities/national-university-of-defense-technology/) — University, Changsha, China · 20 works · 1,339 citations

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## Cite

Cite as: Xiaocheng Zhang, Xi Wang, Yifei Lu, Jianing Wang, Zhuangzhuang Ye, Mengjiao Bao, Peng Yan and Xiaohong Su. 2026. TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2026trendfact,
  title = {{TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking}},
  author = {Xiaocheng Zhang, Xi Wang, Yifei Lu, Jianing Wang, Zhuangzhuang Ye, Mengjiao Bao, Peng Yan and Xiaohong Su},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1219},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1219}
}
```

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25. Category F-D F-C T-CD T-PC T-FC Acc 98.96 96.82 95.68 96.34 *Table 8: Human-LLM agreement rates (%) across five ECS categories, based on manual expert validation of over 100 randomly sampled instances.* [find](https://scholar.google.com/scholar?q=Category+F%2DD+F%2DC+T%2DCD+T%2DPC+T%2DFC+Acc+98%2E96+96%2E82+95%2E68+96%2E34+%2ATable+8%3A+Human%2DLLM+agreement+rates+%28%25%29+across+five+ECS+categories%2C+based+on+manual+expert+validation+of+over+100+randomly+sampled+instances)

*Updated 2 November 2026*