Fake news on social media not only jeopardizes cyberspace security,but also plays a pivotal role in major events,se-verely 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 ad-vancements in enhancing the explainability of detection algorithms.Furthermore,from the adversarial perspective,it deeply analy-zes 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 pros-pected.
Fake news detectionCross-modal correlationSocial context awarenessKnowledge-drivenLarge language model
万方数据
维普
