南京大学学报（自然科学版）2024，Vol.60Issue(6)：970-980,11.DOI:10.13232/j.cnki.jnju.2024.06.008
基于大模型和权威新闻增强的可迭代谣言检测方法
Large language model and real news enhanced iterative rumor detection framework
摘要
Abstract
In the context of the rapidly evolving landscape of social media,where the number of users is substantial and the domains covered by social rumors shift swiftly,the challenge of developing an automated rumor detection system that quickly adapts to emerging domains while maintaining its detection capability remains significant.To address this challenge,this paper proposes an iterative rumor detection framework,LaReF,based on large language models and authoritative news sources.This framework leverages the strengths of large language models in natural language understanding and the credibility of authoritative news through an active learning approach to continuously optimize the rumor detection model.Specifically,LaReF comprises several key modules,an authoritative news retrieval module that enhances the model's detection capability using a dataset of authoritative news,a large language model feature extraction module and a cognitive pattern learning module,which utilizes prompt templates and attention mechanisms to extract features and learn the cognitive patterns of large language models,a feature validity prediction module,which automatically evaluates the importance of each feature and adjusts the weights accordingly,and a multi-feature fusion prediction module that integrates large language model features,sample semantic information,and authoritative news information for rumor detection.Experimental results demonstrate that LaReF exhibits strong performance in rumor detection tasks,effectively identifying the dissemination of rumors in emerging domains on social media.This provides a viable solution for constructing an information security ecosystem in cyberspace.关键词
谣言检测/大语言模型/主动学习/社交媒体/标注成本Key words
rumor detection/large language model/active learning/social media/annotation costs分类
信息技术与安全科学引用本文复制引用
邵浙杰,蔡国永,刘庆华,商云娴..基于大模型和权威新闻增强的可迭代谣言检测方法[J].南京大学学报（自然科学版）,2024,60(6):970-980,11.基金项目
国家自然科学基金(62366010),广西自然科学基金(2024GXNSFAA010374) （62366010）
