基于新闻标题-正文差异性的虚假新闻检测方法  

Detecting Fake News Based on Title-Content Difference

作　　者：刘赏[1] 沈逸凡 Liu Shang;Shen Yifan(School of Science&Technology,Tianjin University of Finance and Economics,Tianjin 300222,China)

机构地区：[1]天津财经大学理工学院,天津300222

出　　处：《数据分析与知识发现》2023年第2期97-107,共11页Data Analysis and Knowledge Discovery

基　　金：教育部人文社会科学研究规划基金项目(项目编号:19YJA630046);天津市自然科学基金项目(项目编号:20JCQNJC00970);天津市艺术科学规划项目(项目编号:C22030)的研究成果之一。

摘　　要：【目的】为解决在虚假新闻检测中新闻评论难以收集、新闻文本简短难以提取有效信息的问题,本文给出一种基于新闻标题和正文差异性的虚假新闻检测方法。【方法】首先,设计Cos-Gap差异性计算方法以获取新闻标题-正文在文本和情感上的差异性特征;然后,根据获得的差异性特征,以异构图注意网络为基础,构建新闻差异性异构图网络NDHN。该网络既包含基于差异性特征构造的边,也包含基于语义特征和情感特征构建的标题、正文和情感三种类型节点。【结果】在GossipCop开放数据集上的实验结果显示,本文提出的检测方法在分类准确率上提升约2.7个百分点,F1指标提升约3.2个百分点。【局限】本文方法适用于带有标题的新闻,对于微博、Twitter等无标题文本存在局限。【结论】融合新闻差异性特征可以有效提升虚假新闻检测准确率,为社交媒体快速检测出虚假新闻提供有力支持。[Objective]This paper proposes a fake news detection method based on the difference between news titles and contents,aiming to address the issues of extracting features from short news texts or retrieving comments.[Methods]Firstly,we designed the Cos-Gap calculation method to obtain the difference between news titles and contents’textual and emotional features.Then,we constructed a News Differential Heterogeneous Graph Network(NDHN)based on the obtained differential features and the Heterogeneous Graph Attention Networks.The NDHN contains edges constructed based on differential features and nodes constructed based on semantic and emotional features of title,content,and emotion.[Results]We examined the proposed model on the GossipCop dataset and found that the NDHN can improve the classification accuracy by 2.7%and the F1 by 3.2%.[Limitations]This method is suitable for analyzing the news with title and has limitations for untitled texts from Sina Weibo or Twitter.[Conclusions]The new model could effectively detect fake news from social media.

关 键 词：虚假新闻检测 异构图网络 差异性特征 舆情分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]