Large language model and real news enhanced iterative rumor detection framework
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.
rumor detectionlarge language modelactive learningsocial mediaannotation costs
国家科技期刊平台
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