MSynFD：多跳语法感知的虚假新闻检测
MSynFD: Multi-hop Syntax aware Fake News Detection
AI总结：
针对现有虚假新闻检测方法忽略文本语法特征、易受先验偏差影响且在模态缺失时性能下降的问题，提出融合多跳语法感知、序列位置Transformer与关键词去偏的MSynFD方法，在两个公开基准上取得了优于现有模型的效果。
摘要：
社交媒体平台的普及推动了虚假新闻的快速传播，对现实社会构成了威胁。现有方法利用多模态数据或上下文信息，通过分析新闻内容和/或其社会语境来提升虚假新闻检测效果。然而，这些方法往往忽略了核心的文本新闻内容（即新闻文章），且严重依赖序列建模和全局注意力机制来提取语义信息。这类现有方法无法应对新闻文章中复杂、细微的扭曲表述，例如语法-语义不匹配以及先验偏差，导致在模态或社会语境缺失时性能下降，甚至可能完全失效。为填补这些显著的研究空白，我们提出了一种新颖的多跳语法感知虚假新闻检测方法MSynFD，该方法引入互补的语法信息以应对虚假新闻中的细微扭曲。具体而言，我们构建了句法依赖图，并设计了多跳子图聚合机制来捕获多跳语法特征。该机制拓展了词语感知的作用范围，能够有效过滤噪声并增强邻接关系。随后，我们设计了一种序列相对位置感知Transformer来捕获序列信息，同时搭配精心设计的关键词去偏模块以缓解先验偏差。在两个公开基准数据集上开展的大量实验结果表明，我们提出的MSynFD方法具备有效性，其性能优于当前最先进的检测模型。
英文摘要：
The proliferation of social media platforms has fueled the rapid dissemination of fake news, posing threats to our real-life society. Existing methods use multimodal data or contextual information to enhance the detection of fake news by analyzing news content and/or its social context. However, these methods often overlook essential textual news content (articles) and heavily rely on sequential modeling and global attention to extract semantic information. These existing methods fail to handle the complex, subtle twists in news articles, such as syntax-semantics mismatches and prior biases, leading to lower performance and potential failure when modalities or social context are missing. To bridge these significant gaps, we propose a novel multi-hop syntax aware fake news detection (MSynFD) method, which incorporates complementary syntax information to deal with subtle twists in fake news. Specifically, we introduce a syntactical dependency graph and design a multi-hop subgraph aggregation mechanism to capture multi-hop syntax. It extends the effect of word perception, leading to effective noise filtering and adjacent relation enhancement. Subsequently, a sequential relative position-aware Transformer is designed to capture the sequential information, together with an elaborate keyword debiasing module to mitigate the prior bias. Extensive experimental results on two public benchmark datasets verify the effectiveness and superior performance of our proposed MSynFD over state-of-the-art detection models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
