[2604.06666] A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM

Computer Science > Computation and Language arXiv:2604.06666 (cs) [Submitted on 8 Apr 2026] Title:A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM Authors:Bo Wang, Jing Ma, Hongzhan Lin, Zhiwei Yang, Ruichao Yang, Yuan Tian, Yi Chang View a PDF of the paper titled A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM, by Bo Wang and 6 other authors View PDF HTML (experimental) Abstract:Explainable fake news detection aims to assess the veracity of news claims while providing human-friendly explanations. Existing methods incorporating investigative journalism are often inefficient and struggle with breaking news. Recent advances in large language models (LLMs) enable leveraging externally retrieved reports as evidence for detection and explanation generation, but unverified reports may introduce inaccuracies. Moreover, effective explainable fake news detection should provide a comprehensible explanation for all aspects of a claim to assist the public in verifying its accuracy. To address these challenges, we propose a graph-enhanced defense framework (G-Defense) that provides fine-grained explanations based solely on unverified reports. Specifically, we construct a claim-centered graph by decomposing the news claim into several sub-claims and modeling their dependency relationships. For each sub-claim, we use the retrieval-augmented generation (RAG) technique to retrieve salient evidence and generate competing explana...