---
title: "When Evidence Falls Short: Router-Guided Fake News Detection with Pattern Augmentation"
authors:
  - "Yujing Wang"
  - "Xiaobao Wang"
  - "Yiqi Dong"
  - "Yueheng Sun"
  - "Di Jin"
  - "Dongxiao He"
doi: "10.24963/ijcai.2026/343"
url: "https://ijcai.org/proceedings/2026/343"
pdf: "https://ijcai.org/proceedings/2026/0343.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "3088-3096"
track: "Main Track"
keywords:
  - "Data Mining: Mining text, web, social media"
  - "Data Mining: Mining graphs"
---

# When Evidence Falls Short: Router-Guided Fake News Detection with Pattern Augmentation

Yujing Wang, Xiaobao Wang, Yiqi Dong, Yueheng Sun, Di Jin, Dongxiao He

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 3088-3096. DOI: [10.24963/ijcai.2026/343](https://doi.org/10.24963/ijcai.2026/343)

## 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.