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

• URL: http://arxiv.org/abs/2604.06666v1
• Date: Wed, 08 Apr 2026 04:34:19 GMT
• Title: A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM
• Abstract summary: Explainable fake news detection aims to assess the veracity of news claims while providing human-friendly explanations.<n>Recent advances in large language models (LLMs) enable leveraging externally retrieved reports as evidence for detection and explanation generation.<n>We propose a graph-enhanced defense framework (G-Defense) that provides fine-grained explanations based solely on decomposing unverified reports.
• Score: 25.505981493236934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 explanations. We then introduce a defense-like inference module based on the graph to assess the overall veracity. Finally, we prompt an LLM to generate an intuitive explanation graph. Experimental results demonstrate that G-Defense achieves state-of-the-art performance in both veracity detection and the quality of its explanations.

Related papers

• VeriGraph: Towards Verifiable Data-Analytic Agents [71.17693022916896]
  LLM-based agents have demonstrated strong capabilities in data-intensive analytical tasks, yet their outputs are rarely verifiable.<n>We propose VeriGraph, a traceable neuro-symbolic reasoning framework that enables agents to construct an explicit heterogeneous evidence directed acyclic graph.<n>Our results suggest that explicit evidence-graph construction is a promising path toward verifiable data-analytic agents.
  arXiv  Detail & Related papers  (2026-06-15T11:50:56Z)
• Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking [47.47518672198846]
  Misinformation spreading over the Internet poses a significant threat to both societies and individuals.<n>Previous methods rely on semantic and social-contextual patterns learned from training data.<n>We propose WKGFC, which exploits authorized open knowledge graph as a core resource of evidence.
  arXiv  Detail & Related papers  (2026-02-27T19:29:01Z)
• Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection [53.41813030290324]
  Few-Shot Fake News Detection (FS-FND) aims to distinguish inaccurate news from real ones in extremely low-resource scenarios.<n>This task has garnered increased attention due to the widespread dissemination and harmful impact of fake news on social media.<n>We propose a Dual-perspective Knowledge-guided Fake News Detection (DKFND) model, designed to enhance LLMs from both inside and outside perspectives.
  arXiv  Detail & Related papers  (2024-07-12T03:15:01Z)
• Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom [19.027690459807197]
  We propose a novel defense-based explainable fake news detection framework.<n> Specifically, we first propose an evidence extraction module to split the wisdom of crowds into two competing parties and respectively detect salient evidences.<n>We then design a prompt-based module that utilizes a large language model to generate justifications by inferring reasons towards two possible veracities.
  arXiv  Detail & Related papers  (2024-05-06T11:24:13Z)
• Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges [21.425647152424585]
  We propose a strong fake news attack method called conditional Variational-autoencoder-Like Prompt (VLPrompt) Unlike current methods, VLPrompt eliminates the need for additional data collection while maintaining contextual coherence. Our experiments, including various detection methods and novel human study metrics, were conducted to assess their performance on our dataset.
  arXiv  Detail & Related papers  (2024-03-27T04:39:18Z)
• Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection [50.07850264495737]
  "Prompt-and-Align" (P&A) is a novel prompt-based paradigm for few-shot fake news detection. We show that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.
  arXiv  Detail & Related papers  (2023-09-28T13:19:43Z)
• Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks [20.282527436527765]
  We propose a unified Graph-based sEmantic structure mining framework with ConTRAstive Learning, namely GETRAL in short. We first model claims and evidences as graph-structured data to capture the long-distance semantic dependency. Then the fine-grained semantic representations are fed into the claim-evidence interaction module for predictions.
  arXiv  Detail & Related papers  (2022-10-11T14:54:37Z)
• A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection [15.517424861844317]
  Existing fake news detection methods aim to classify a piece of news as true or false and provide explanations, achieving remarkable performances. When a piece of news has not yet been fact-checked or debunked, certain amounts of relevant raw reports are usually disseminated on various media outlets. We propose a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports.
  arXiv  Detail & Related papers  (2022-09-29T09:05:47Z)
• Mining Fine-grained Semantics via Graph Neural Networks for Evidence-based Fake News Detection [20.282527436527765]
  We propose a unified Graph-based sEmantic sTructure mining framework, namely GET in short. We model claims and evidences as graph-structured data and capture the long-distance semantic dependency. After obtaining contextual semantic information, our model reduces information redundancy by performing graph structure learning.
  arXiv  Detail & Related papers  (2022-01-18T11:28:36Z)
• Jointly Attacking Graph Neural Network and its Explanations [50.231829335996814]
  Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs. We propose a novel attack framework (GEAttack) which can attack both a GNN model and its explanations by simultaneously exploiting their vulnerabilities.
  arXiv  Detail & Related papers  (2021-08-07T07:44:33Z)
• AmbiFC: Fact-Checking Ambiguous Claims with Evidence [57.7091560922174]
  We present AmbiFC, a fact-checking dataset with 10k claims derived from real-world information needs. We analyze disagreements arising from ambiguity when comparing claims against evidence in AmbiFC. We develop models for predicting veracity handling this ambiguity via soft labels.
  arXiv  Detail & Related papers  (2021-04-01T17:40:08Z)
• Machine Learning Explanations to Prevent Overtrust in Fake News Detection [64.46876057393703]
  This research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining.
  arXiv  Detail & Related papers  (2020-07-24T05:42:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.