社交媒体虚假信息检测研究综述  

Review of Fake News Detection on Social Media

作　　者：陈静[1] 周刚 李顺航 郑嘉丽 卢记仓 郝耀辉[1] CHEN Jing;ZHOU Gang;LI Shunhang;ZHENG Jiali;LU Jicang;HAO Yaohui(School of Data and Target Engineering,Information Engineering University,Zhengzhou 450001,China)

机构地区：[1]信息工程大学数据与目标工程学院,郑州450001

出　　处：《计算机科学》2024年第11期1-14,共14页Computer Science

基　　金：国家自然科学基金面上项目(62172433);河南省科技攻关项目(222102210081);河南省软科学研究项目(202400410084)。

摘　　要：社交媒体上的虚假信息不仅危害网络空间安全,还在重大事件中扮演着关键角色,严重误导公众,对政治和社会秩序造成负面影响。为此,围绕面向社交媒体的虚假信息检测技术研究展开综述,为构建高效检测技术和遏制社交媒体虚假信息泛滥奠定理论基础。首先,深入剖析虚假信息的内涵本质,探讨其在社交平台上的产生机理、具体表现形式,并界定检测任务的基础框架与目标;其次,从语义一致性视角出发,专注内容语义、社交上下文感知和知识驱动三大层面,对比梳理典型检测方法;在此基础之上,深入探究增强检测算法可解释性最新研究成果;进一步,从对抗博弈视角,深度剖析当前社交媒体虚假信息检测任务面临的挑战以及大型语言模型为虚假信息检测技术研究带来的机遇;最后,对社交媒体虚假信息检测技术未来的发展进行了展望。Fake news on social media not only jeopardizes cyberspace security,but also plays a pivotal role in major events,severely misleads the public and has a negative affect on political and social order.Therefore,this paper outlines social media fake news detection techniques,establishing a theoretical foundation for building efficient detection technology and curbing the proli-feration of fake news on social media.Firstly,it deeply analyzes the connotation and essence of fake news,explores its generation mechanism and specific manifestations on social platforms,and defines the basic framework and objectives of the detection task.Next,from the perspective of semantic consistency,it focuses on three major levels:content semantics,social context awareness,and knowledge-driven,and compares and combs typical detection methods.On this basis,it deeply explores the latest research advancements in enhancing the explainability of detection algorithms.Furthermore,from the adversarial perspective,it deeply analyzes the challenges faced by current social media fake news detection tasks and the opportunities brought to research detection technology by large-scale language models.Finally,the future development of social media fake news detection technology is prospected.

关 键 词：虚假信息检测 跨模态关联 社交上下文感知 知识驱动 大语言模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]