多模态多层次事件网络的谣言检测  

Multi-modal multi-level event network for rumor detection

作　　者：李莎[1] 张怀文[2] 钱胜胜 方全[2] 徐常胜[2] Li Sha;Zhang Huaiwen;Qian Shengsheng;Fang Quan;Xu Changsheng(Zhengzhou University,Zhengzhou 450000,China;National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]郑州大学,郑州450000 [2]中国科学院自动化研究所模式识别国家重点实验室,北京100190

出　　处：《中国图象图形学报》2021年第7期1648-1657,共10页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61802405,61832002)。

摘　　要：目的自动检测谣言至关重要,目前已有多种谣言检测方法,但存在以下两点局限:1)只考虑文本内容,忽略了可用于判断谣言的辅助多模态信息;2)只关注时间序列模型捕捉谣言事件的时间特征,没有很好地研究事件的局部信息和全局信息。为了克服这些局限性,有效利用多模态帖子信息并联合多种编码策略构建每个新闻事件的表示,本文提出一种新颖的基于多模态多层次事件网络的社交媒体谣言检测方法。方法通过一个多模态的帖子嵌入层,同时利用文本内容和视觉内容;将多模态的帖子嵌入向量送入多层次事件编码网络,联合使用多种编码策略,以由粗到细的方式描述事件特征。结果在Twitter和Pheme数据集上的大量实验表明,本文提出的多模态多层次事件网络模型比现有的SVM-TS(support vector machine—time structure)、CNN(convolutional neural network)、GRU(gated recurrent unit)、CallAtRumors和MKEMN(multimodal knowledge-aware event memory network)等方法在准确率上提升了4%以上。结论本文提出的谣言检测模型,对每个事件的全局、时间和局部信息进行建模,提升了谣言检测的性能。Objective The proliferation of social media has revolutionized the way people acquire information.A growing number of people choose to share information,and express and exchange opinions through social media.Unfortunately,because a large number of users do not carefully verify the released content when posting information and sharing their opinions,various rumors have been fostered on social media platforms.The extensive spread of these rumors is expected to bring new threats to the political,economic,and cultural fields and affect people’s lives.To strengthen the detection of rumors and prevent their spread,many approaches to rumor detection have been proposed.An early rumor detection platform(e.g.,snopes.com)mainly reported through users,and then invited experts or institutions in related fields to confirm.Although these methods can achieve the purpose of rumor detection,the timeliness of detection has obvious limitations.Thus,how to detect rumors automatically has become a key research direction in recent years.To date,many automatic detection approaches have been proposed to improve the efficiency of rumor detection,including feature constructionbased and neural network-based methods.The feature construction-based methods rely on hand-craft features to train rumor classifiers and neural network-based methods using neural networks to automatically extract deep features.Compared with traditional methods,models based on deep neural networks can automatically learn the underlying deep representation of rumors and extract more effective semantic features.However,these methods may suffer from the following limitations.1)At post level,many existing methods only consider the text content.In fact,posts often contain various types of information(e.g.,text and images),and the visual information are often used as an auxiliary information to judge the credibility of posts in reality.Therefore,the key to detecting rumors is obtaining the multi-modal information of the posts and systematically integrating the textual and

关 键 词：多模态 谣言检测 社交媒体 多层次编码策略 事件网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]