基于多模态学习的虚假新闻检测研究

Survey of Fake News Detection with Multi-model Learning

社交媒体在给人们带来便利的同时,也成为虚假新闻恣意传播的渠道,如果不及时发现遏止,极易引发群众恐慌,激起社会动荡。因此,探索准确高效的虚假新闻检测技术具有极高的理论价值和现实意义。对虚假新闻相关检测技术做了全面综述。首先,对多模态虚假新闻的相关概念进行了整理和归纳,并分析了单模态和多模态新闻数据集的变化趋势。其次,介绍了基于机器学习和深度学习的单模态虚假新闻检测技术,这些技术在虚假新闻检测领域已被广泛应用,而由于虚假新闻通常包含多种数据表现形式,这些传统的单模态技术无法充分挖掘虚假新闻的深层逻辑,因此无法有效地应对多模态虚假新闻数据带来的挑战。针对此问题,对近些年来先进的多模态虚假新闻检测技术进行了整理,从多流架构和图架构的角度归纳和论述了这些多模态检测的技术方法,探讨了这些技术的思想理念与潜在缺陷。最后,分析了目前虚假新闻检测研究领域存在的困难和瓶颈,并由此给出未来的研究方向。

While social media brings convenience to people,it has also become a channel for the arbitrary spread of fake news.If not detected and stopped in time,it is easy to cause public panic and social unrest.Therefore,exploring accurate and efficient fake news detection technology has high theoretical value and practical significance.This paper provides a comprehensive overview of the related fake news detection techniques.Firstly,the relevant concepts of multi-modal fake news are sorted and summarized,and the trend of changes in single-modal and multi-modal news datasets is analyzed.Secondly,this paper introduces single-modal fake news detection techniques based on machine learning and deep learning,which have been widely used in the field of fake news detection.However,traditional single-modal techniques cannot fully explore the deep logic of fake news because fake news usually contains multiple data presentations.Thus,they are unable to effectively deal with the challenges brought by multi-modal fake news data.To address this issue,this paper summarizes and discusses advanced multi-modal fake news detection techniques from the perspectives of multi-stream and graph architectures,and explores their concepts and potential drawbacks.Finally,this paper analyzes the difficulties and bottlenecks in the current research on fake news detection and provides future research directions based on these analyses.