发表机构
Nanjing University of Science and Technology; Zhejiang University; University of Chinese Academy of Sciences（南京理工大学; 浙江大学; 中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有假新闻检测依赖实体级检索易引入噪声且忽视危害差异的问题，提出事件级证据检索框架ELERF和关系感知证据图网络RAEGNet，通过建模新闻-证据关系及条件危害分支，在多个数据集上取得最优性能。
AI 中文摘要
现有的多模态假新闻检测方法通常引入外部信息来辅助检测。然而，大多数方法依赖于实体级检索，因此容易引入与事件无关的噪声。同时，现有方法主要关注提升整体性能，并未考虑不同假新闻实例所造成的危害程度差异。为解决这些局限，我们设计了一个事件级证据检索框架（ELERF），并提出了一种关系感知证据图网络（RAEGNet）。ELERF基于新闻项的完整事件语义检索外部证据。RAEGNet构建了一个有向图，融合了新闻-证据立场关系和证据-证据交互关系，并引入了一个条件危害分支来联合建模真实性和潜在危害。实验结果表明，在Weibo-21、Fakeddit以及我们自建的SSS数据集上，RAEGNet在所有评估指标上均优于多个基线方法。
英文摘要
Existing multimodal fake news detection methods often introduce external information to assist detection. However, most of them rely on entity-level retrieval and are therefore prone to introducing event-irrelevant noise. Meanwhile, existing methods mainly focus on improving overall performance and do not account for differences in the degree of harm posed by different instances of fake news. To address these limitations, we design an Event-Level Evidence Retrieval Framework (ELERF) and propose a Relation-Aware Evidence Graph Network (RAEGNet). ELERF retrieves external evidence based on the complete event semantics of a news item. RAEGNet constructs a directed graph that incorporates news-evidence stance relations and evidence-evidence interaction relations, and introduces a conditional-harm branch to jointly model authenticity and potential harm. Experimental results demonstrate that RAEGNet outperforms multiple baseline methods across all evaluated metrics on Weibo-21, Fakeddit, and our self-constructed SSS dataset.
