利用LLM生成的解释来检测情感改写的虚假新闻

arXiv cs.CL 论文
fake-news-detection large-language-models emotion-rewriting explanation-generation cross-attention misinformation

摘要

本文提出了一种门控交叉注意力（Gated Cross Attention, GCA）框架，利用LLM生成的解释作为稳定的背景知识，以检测在保留原始事实主张的同时被不同情感框架改写的虚假新闻，并在PolitiFact、GossipCop和LUN数据集上取得了性能提升。

arXiv:2610.08835v1 Announce Type: new Abstract: The spread of fake news may cause severe social consequences. Existing fake news detection methods mainly focus on stylistic variations or incorporate external information such as explanations. However, news articles are often rewritten under different emotional backgrounds while preserving their underlying factual claims, which may affect the robustness of detection models. In this work, we investigate fake news detec- tion under fact-preserving emotional variations. To study this problem, we construct emotion-rewritten test sets and generate explanations from the original news articles as stable background knowledge. We then propose a Gated Cross Attention (GCA) framework that adaptively integrates emotionally rewritten news with the corresponding explanations, enabling the model to focus on informative explanation content while reducing potential mismatches caused by emotional reframing. Experiments on PolitiFact, GossipCop, and LUN demonstrate that the proposed method achieves notable improvements under multiple emotional conditions on PolitiFact and LUN, while maintaining competitive performance on GossipCop. We further analyze the effects of explanation guidance and gating mechanisms under different emotional conditions. Our code and data are available at: https://github.com/Flulike/fakenews gca .
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# Leveraging LLM-Generated Explanations for Detecting Emotionally Rewritten Fake News
Source: [https://arxiv.org/abs/2610.08835](https://arxiv.org/abs/2610.08835)
[View PDF](https://arxiv.org/pdf/2610.08835)[HTML \(experimental\)](https://arxiv.org/html/2610.08835v1)

> Abstract:The spread of fake news may cause severe social consequences\. Existing fake news detection methods mainly focus on stylistic variations or incorporate external information such as explanations\. However, news articles are often rewritten under different emotional backgrounds while preserving their underlying factual claims, which may affect the robustness of detection models\. In this work, we investigate fake news detec\- tion under fact\-preserving emotional variations\. To study this problem, we construct emotion\-rewritten test sets and generate explanations from the original news articles as stable background knowledge\. We then propose a Gated Cross Attention \(GCA\) framework that adaptively integrates emotionally rewritten news with the corresponding explanations, enabling the model to focus on informative explanation content while reducing potential mismatches caused by emotional reframing\. Experiments on PolitiFact, GossipCop, and LUN demonstrate that the proposed method achieves notable improvements under multiple emotional conditions on PolitiFact and LUN, while maintaining competitive performance on GossipCop\. We further analyze the effects of explanation guidance and gating mechanisms under different emotional conditions\. Our code and data are available at:[this https URL](https://github.com/Flulike/fakenews)gca \.

## Submission history

From: Zekun Yang \[[view email](https://arxiv.org/show-email/40f8cd3e/2610.08835)\] **\[v1\]**Tue, 29 Sep 2026 11:50:30 UTC \(852 KB\)

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