{"type":"ScholarlyArticle","title":"When Evidence Falls Short: Router-Guided Fake News Detection with Pattern Augmentation","authors":[{"name":"Yujing Wang","slug":"yujing-wang","given":"Yujing","family":"Wang","affiliation":""},{"name":"Xiaobao Wang","slug":"xiaobao-wang","given":"Xiaobao","family":"Wang","affiliation":""},{"name":"Yiqi Dong","slug":"yiqi-dong","given":"Yiqi","family":"Dong","affiliation":""},{"name":"Yueheng Sun","slug":"yueheng-sun","given":"Yueheng","family":"Sun","affiliation":""},{"name":"Di Jin","slug":"di-jin","given":"Di","family":"Jin","affiliation":""},{"name":"Dongxiao He","slug":"dongxiao-he","given":"Dongxiao","family":"He","affiliation":""}],"abstract":"With the growing complexity of online information, trustworthy fake news detection has become increasingly critical. Although Large Language Models (LLMs) exhibit a strong ability to leverage factual evidence for verification, they remain highly vulnerable to unreliable, noisy, or scarce evidence, undermining robustness in real-world scenarios. Given the generalizability of deceptive patterns in fake news, we consider pattern as a complementary signal under insufficient evidence during factual verification. However, due to LLMs' lack of expertise in deception-specific patterns, realizing such effective collaboration remains challenging. To address these issues, we propose a Router-Guided Fake News Detection Framework with Pattern Augmentation (RGPA). Specifically, we introduce a hierarchical routing mechanism including a case router and an external evidence router. It guides news to appropriate reasoning paths adaptively based on a multi-dimensional quality assessment, prioritizing high-quality evidence while mitigating noise. Furthermore, we design an expert model to capture deceptive features and integrate them into LLMs' reasoning, enabling a synergy of factual verification and pattern awareness under evidence-scarce scenarios. Extensive experiments on two real-world datasets demonstrate that RGPA significantly outperforms existing approaches.","doi":"10.24963/ijcai.2026/343","url":"https://ijcai.org/proceedings/2026/343","pdf":"https://ijcai.org/proceedings/2026/0343.pdf","markdown":"https://ijcai.org/proceedings/2026/343.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/343","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":343,"pages":{"first":3088,"last":3096},"keywords":["Data Mining: Mining text, web, social media","Data Mining: Mining graphs"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}