标题： 基于大语言模型的结构感知传播生成用于虚假新闻检测 显示英文标题

标题： Structure-aware Propagation Generation with Large Language Models for Fake News Detection

摘要： 社交媒体上虚假新闻的传播对公众信任和社会稳定构成了严重威胁。 虽然基于传播的方法通过建模信息如何传播来提高虚假新闻检测效果，但它们通常受到不完整的传播数据的困扰。 最近的工作利用大型语言模型（LLMs）生成合成传播，但通常忽略了现实世界讨论的结构模式。 在本文中，我们提出了一种新的结构感知的合成传播增强检测（StruSP）框架，以全面捕捉真实传播中的结构动态。 它使LLMs能够生成现实且结构一致的传播，以实现更好的检测。 StruSP在语义和结构维度上明确地将合成传播与现实世界的传播对齐。 此外，我们还设计了一种新的双向进化传播（BEP）学习策略，通过结构感知的混合采样和掩码传播建模目标，更好地使LLMs与现实世界中的传播结构模式对齐。 在三个公共数据集上的实验表明，StruSP在各种实际检测场景中显著提高了虚假新闻检测性能。 进一步分析表明，BEP使LLMs能够生成在语义和结构上更加真实和多样的传播。 显示英文摘要

摘要： The spread of fake news on social media poses a serious threat to public trust and societal stability. While propagation-based methods improve fake news detection by modeling how information spreads, they often suffer from incomplete propagation data. Recent work leverages large language models (LLMs) to generate synthetic propagation, but typically overlooks the structural patterns of real-world discussions. In this paper, we propose a novel structure-aware synthetic propagation enhanced detection (StruSP) framework to fully capture structural dynamics from real propagation. It enables LLMs to generate realistic and structurally consistent propagation for better detection. StruSP explicitly aligns synthetic propagation with real-world propagation in both semantic and structural dimensions. Besides, we also design a new bidirectional evolutionary propagation (BEP) learning strategy to better align LLMs with structural patterns of propagation in the real world via structure-aware hybrid sampling and masked propagation modeling objective. Experiments on three public datasets demonstrate that StruSP significantly improves fake news detection performance in various practical detection scenarios. Further analysis indicates that BEP enables the LLM to generate more realistic and diverse propagation semantically and structurally.