Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection

• URL: http://arxiv.org/abs/2509.01660v1
• Date: Mon, 01 Sep 2025 17:59:18 GMT
• Title: Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection
• Authors: Zhengjia Wang, Qiang Sheng, Danding Wang, Beizhe Hu, Juan Cao,
• Abstract summary: We propose Graph-based Intent-Semantic Joint Modeling (InSide) for fake news detection.<n>InSide reformulates news semantic and intent signals into heterogeneous graph structures.
• Score: 16.22231659861359
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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