计算机工程2026，Vol.52Issue(5)：60-80,21.DOI:10.19678/j.issn.1000-3428.0070287
社交媒体虚假信息检测技术研究综述
Technologies for Detecting Disinformation in Social Media:A Comprehensive Review
摘要
Abstract
Timely and effective disinformation detection is crucial for curbing the spread of disinformation and minimizing social harm.Numerous deep learning methods have been employed for disinformation detection.Summarizing the detection principles and paradigms of existing research is essential for identifying directions for technical optimization.Therefore,this paper comprehensively reviews existing research based on the principles and implementation paths of disinformation detection,and for the first time,summarizes and compares the applications of large language models in this field.First,the relevant concepts of disinformation detection tasks are introduced and the data structures of commonly used disinformation detection datasets are summarized.Then,based on detection principles and implementation methods,the paper presents ways to detect textual and multimodal disinformation through semantic feature representation,auxiliary task design,internal knowledge inference,and fact verification,refining them into ten subcategories and summarizing the potential characteristics of detection methods for each subcategory.Finally,the paper summarizes disinformation detection paradigms based on deep neural networks and large language models,compares the detection performance of representative methods from these paradigms across seven disinformation detection datasets,and highlights the advantages and limitations of large language models in detecting disinformation.It also presents the anticipated opportunities and challenges brought about by large language models in the field of disinformation detection,providing a reference for future research.关键词
深度学习/自然语言处理/虚假信息检测/大语言模型/事实核查Key words
deep learning/natural language processing/disinformation detection/large language models/fact checking分类
信息技术与安全科学引用本文复制引用
许旻辰,屈丹,司念文,彭思思,陈雅淇..社交媒体虚假信息检测技术研究综述[J].计算机工程,2026,52(5):60-80,21.基金项目
国家自然科学基金(62171470) （62171470）
河南省中原科技创新领军人才项目(234200510019) （234200510019）
河南省自然科学基金面上项目(232300421240). （232300421240）
