# TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking

Source: [TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking](<https://www.sota2.com/research/paper/68fa78b41e209131cea79de6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-07-08T05:00:13.057Z

Authors: Xiaocheng Zhang, Xi Wang, Yifei Lu, Jianing Wang, Zhuangzhuang Ye, Mengjiao Bao, Peng Yan, Xiaohong Su

Published: 2024-10-19T15:25:19.000Z

## Abstract

With the surge of online misinformation, Large Language Models (LLMs) and Reasoning Large Language Models (RLMs) serving as Automatic Fact-Checking (AFC) systems have emerged as a prominent paradigm for reliable, explainable verification. However, our empirical study reveals that this paradigm faces a critical risk asymmetry challenge when deployed in the real world under resource-constrained environments. While Hotspot Perception Ability (HPA), the capacity to dynamically allocate reasoning resources based on social impact, is essential to mitigate this risk, existing benchmarks lack the social metadata and evaluation framework to meet this urgent evaluation needs, thereby hindering the advancement of these AFC systems. To bridge this gap, we introduce TrendFact, the first benchmark capable of evaluating HPA and three fact-checking tasks. It consists of 7,643 curated samples sourced from trending platforms and professional datasets, with an evidence library containing 366,634 entries. To enable HPA assessment, we propose two novel metrics: the Explanation Consistency Score (ECS) to evaluate the reliability of verification reasoning, and the Hotspot Claim Perception Index (HCPI) to quantify the overall HPA of AFC systems. Extensive experiments demonstrate that existing AFC systems exhibit limited performance on TrendFact. Furthermore, our proposed FactISR framework effectively enhances HPA and computational efficiency for RLMs-served AFC systems.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Explanation Generation on TrendFact (test)](<https://www.sota2.com/research/sota/explanation-generation-on-trendfact-test.md>): Explanation Generation; TrendFact (test); ECS: 86.1.

- [Fact Verification on TrendFact](<https://www.sota2.com/research/sota/fact-verification-on-trendfact.md>): Fact Verification; TrendFact; F1 Score: 61.17.

- [Fact Verification on TrendFact (test)](<https://www.sota2.com/research/sota/fact-verification-on-trendfact-test.md>): Fact Verification; TrendFact (test); Accuracy: not recorded.

- [Evidence Retrieval on TrendFact (test)](<https://www.sota2.com/research/sota/evidence-retrieval-on-trendfact-test.md>): Evidence Retrieval; TrendFact (test); R@1: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2410.15135>)

[PDF](<https://arxiv.org/pdf/2410.15135.pdf>)