LLM知识增强与特征门控融合的虚假信息检测OA

In response to the challenges of detecting social media misinformation under conditions of implicit semantic expression,complex content structures,and diverse propagation behaviors,this paper investigates a unified detection framework that integrates the reasoning capabilities of large language models with multi-source feature representations.The objective is to improve misinformation detection accuracy through the joint modeling of reasoning-based knowledge and multi-dimensional features,while enhancing the transparency of the model's decision process.A large language model-enhanced dual-gated fusion framework(LLM-EDGF)is developed,in which a structured prompting procedure is employed to guide the large language model to generate reasoning-based knowledge features related to factual consis-tency and logical coherence of the text,which are then jointly modeled with deep semantic representations extracted by BERT.In addition,user profile features and propagation behavior features are incorporated to characterize the attributes of information publishers and diffusion patterns.At the feature fusion stage,a two-stage gating mechanism is designed at both the"knowledge-semantic"and"content-social"levels to achieve dynamic weighting of multi-source features and suppression of noisy information,and a lightweight classifier is finally applied for misinformation detection.Experimen-tal results on multiple public datasets and a self-constructed Weibo dataset demonstrate that the proposed framework out-performs comparative models in overall detection performance and exhibits more stable recognition capability in complex misinformation scenarios such as partial fabrication of facts and incoherent narrative structures.Furthermore,the intro-duced reasoning-based knowledge features provide traceable information for the model's decision process,supporting the applicability of the proposed approach to social media misinformation detection tasks.