发表机构
The University of Osaka; Alibaba Inc; Nagoya University; Tokyo University of Science（大阪大学; 阿里巴巴公司; 名古屋大学; 东京理科大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对情感改写保留事实的假新闻，提出门控交叉注意力框架，利用LLM生成的解释作为稳定背景知识，在PolitiFact和LUN上显著提升检测性能，在GossipCop上保持竞争力。
AI 中文摘要
假新闻的传播可能造成严重的社会后果。现有的假新闻检测方法主要关注文体变化或整合外部信息（如解释）。然而，新闻文章常常在不同情感背景下被改写，同时保留其潜在的事实主张，这可能影响检测模型的鲁棒性。在本工作中，我们研究在保留事实的情感变化下的假新闻检测。为研究此问题，我们构建了情感改写测试集，并从原始新闻文章中生成解释作为稳定的背景知识。随后，我们提出了一种门控交叉注意力（GCA）框架，该框架自适应地整合情感改写的新闻与相应的解释，使模型能够聚焦于信息丰富的解释内容，同时减少由情感重构引起的潜在不匹配。在PolitiFact、GossipCop和LUN上的实验表明，所提方法在PolitiFact和LUN的多种情感条件下取得了显著改进，同时在GossipCop上保持了有竞争力的性能。我们进一步分析了不同情感条件下解释引导和门控机制的效果。我们的代码和数据可在以下网址获取：this https URL gca。
英文摘要
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 .
