Abstract
Fake news detection is an important and challenging task for defending online information integrity. Existing state-of-the-art approaches typically extract news semantic clues, such as writing patterns that include emotional words, stylistic features, etc. However, detectors tuned solely to such semantic clues can easily fall into surface detection patterns, which can shift rapidly in dynamic environments, leading to limited performance in the evolving news landscape. To address this issue, this paper investigates a novel perspective by incorporating news intent into fake news detection, bridging intents and semantics together. The core insight is that by considering news intents, one can deeply understand the inherent thoughts behind news deception, rather than the surface patterns within words alone. To achieve this goal, we propose Graph-based Intent-Semantic Joint Modeling (InSide) for fake news detection, which models deception clues from both semantic and intent signals via graph-based joint learning. Specifically, InSide reformulates news semantic and intent signals into heterogeneous graph structures, enabling long-range context interaction through entity guidance and capturing both holistic and implementation-level intent via coarse-to-fine intent modeling. To achieve better alignment between semantics and intents, we further develop a dynamic pathway-based graph alignment strategy for effective message passing and aggregation across these signals by establishing a common space. Extensive experiments on four benchmark datasets demonstrate the superiority of the proposed InSide compared to state-of-the-art methods.
Abstract (translated)
虚假新闻检测是一项重要且具有挑战性的任务，旨在保护在线信息的完整性。现有的最先进的方法通常会提取诸如包含情感词汇的写作模式和风格特征等新闻语义线索。然而，仅针对此类语义线索调优的检测器容易陷入表面检测模式，在动态环境中这些模式变化迅速，从而在不断演变的新闻景观中表现有限。为了解决这个问题，本文从一个新颖的角度出发，通过将新闻意图纳入虚假新闻检测来探讨这一问题，旨在连接意图和语义之间的联系。核心见解在于：考虑新闻意图可以深入了解其背后的内在思维，而不仅仅是表面的文字模式。 为了实现这一目标，我们提出了一种基于图的意图-语义联合建模方法（InSide），用于假新闻检测。该模型通过基于图的联合学习方式从语义和意图信号中提取欺骗线索。具体而言，InSide将新闻的语义和意图信号重新表述为异构图结构，这使长距离上下文交互成为可能，并且能够通过粗到细级别的意图建模捕捉全面及实施层面的意图。 为了更好地实现语义与意图之间的对齐，我们进一步开发了一种基于动态路径图对齐策略的方法，以有效进行跨信号的消息传递和聚合，在此过程中建立了一个公共空间。在四个基准数据集上的广泛实验证明了所提出的InSide方法优于现有最先进的方法。
URL
https://arxiv.org/abs/2509.01660
