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Proceedings of the AAAI Conference on Artificial Intelligence

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FACTGUARD: Event-Centric and Commonsense-Guided Fake News...
Jing He, Han · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Jing He School of Software and AI, Yunnan University
  • Han Zhang School of Software and AI, Yunnan University
  • Yuanhui Xiao School of Software and AI, Yunnan University
  • Wei Guo School of Software and AI, Yunnan University
  • Shaowen Yao School of Software and AI, Yunnan University
  • Renyang Liu Institute of Data Science, National University of Singapore

DOI:

https://doi.org/10.1609/aaai.v40i1.36998

Abstract

Fake news detection methods based on writing style have achieved remarkable progress. However, as adversaries increasingly imitate the style of authentic news, the effectiveness of such approaches is gradually diminishing. Recent research has explored incorporating large language models (LLMs) to enhance fake news detection. Yet, despite their transformative potential, LLMs remain an untapped goldmine for fake news detection, with their real-world adoption hampered by shallow functionality exploration, ambiguous usability, and prohibitive inference costs. In this paper, we propose a novel fake news detection framework, dubbed FACTGUARD, that leverages LLMs to extract event-centric content, thereby reducing the impact of writing style on detection performance. Furthermore, our approach introduces a dynamic usability mechanism that identifies contradictions and ambiguous cases in factual reasoning, adaptively incorporating LLM advice to improve decision reliability. To ensure efficiency and practical deployment, we employ knowledge distillation to derive FACTGUARD-D, enabling the framework to operate effectively in cold-start and resource-constrained scenarios. Comprehensive experiments on two benchmark datasets demonstrate that our approach consistently outperforms existing methods in both robustness and accuracy, effectively addressing the challenges of style sensitivity and LLM usability in fake news detection.

How to Cite

He, J., Zhang, H., Xiao, Y., Guo, W., Yao, S., & Liu, R. (2026). FACTGUARD: Event-Centric and Commonsense-Guided Fake News Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 363–371. https://doi.org/10.1609/aaai.v40i1.36998

Issue

Section

AAAI Technical Track on Application Domains I

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