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.

翻译：社交媒体平台的激增加速了虚假新闻的快速传播，对现实社会构成威胁。现有方法利用多模态数据或上下文信息，通过分析新闻内容及其社交语境来增强虚假新闻检测。然而，这些方法往往忽视关键的文本新闻内容（文章），并过度依赖序列建模和全局注意力来提取语义信息。现有方法难以处理新闻文章中复杂而微妙的扭曲现象，如语法-语义失配和先验偏差，导致在缺失模态或社交语境时性能下降甚至失效。为弥补这些重要不足，我们提出一种新颖的多跳语法感知虚假新闻检测方法（MSynFD），该方法通过整合互补的语法信息来处理虚假新闻中的微妙扭曲。具体而言，我们引入语法依赖图并设计多跳子图聚合机制以捕获多跳语法关系。该机制扩展了词汇感知的效力，从而实现有效的噪声过滤与邻近关系增强。随后，我们设计了具有序列相对位置感知能力的Transformer来捕获序列信息，并构建了精细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的大量实验结果验证了所提MSynFD相较于最先进检测模型的有效性与优越性能。