摘要
随着以微博为代表的社交媒体越来越流行,谣言信息借助社交媒体迅速传播,容易造成严重的后果,因此自动谣言检测问题受到了国内外学术界、产业界的广泛关注。目前,越来越多的用户使用图片来发布微博,而不仅仅是文本,微博通常由文本、图像和社会语境组成。因此,文中提出了一种基于深度神经网络,针对配文文本内容、图像以及用户属性信息的多模态网络谣言检测方法DCNN。该方法由多模态特征提取器和谣言检测器组成,多模态特征提取器分为3部分,即基于TextCNN的文本特征提取器、基于VGG-19的图片特征提取器和基于DeepFM算法的用户社会特征提取器,分别用于学习微博不同模态上的特征表示,以形成重新参数化的多模态特征,特征融合后将该融合后的多模态特征作为谣言检测器的输入进行分类检测。在微博数据集上对该算法进行了大量实验,实验结果表明DCNN算法将识别准确率从78.1%提高到了80.3%,验证了DCNN算法和其中对社会特征建立特征交互方法的可行性与有效性。
With the increasing popularity of social media represented by Weibo,rumors spread rapidly through social media,which is more likely to cause serious consequences.The problem of automatic rumor detection has attracted widespread attention from academic and industrial circles at home and abroad.We have noticed that more and more users use pictures to post Weibo,not just text.Weibo usually consists of text,images and social context.Therefore,a multi-modal network rumor detection method DCNN based on deep neural network for the text content,image and user attribute information of the accompanying text is proposed.This method consists of a multi-modal feature extractor and a rumor detector.The multi-modal feature extractor is divided into three parts:a text feature extractor based on TextCNN,apicture feature extractor based on VGG-19,and a user social feature extractor based on DeepFM algorithm.These three parts learn feature representations on different modalities of Weibo to form re-parameterized multi-modal features,which are fused as input to the rumor detector classification detection.This algorithm has carried out a large number of experiments on the Weibo data set,and the experimental results show that the recognition accuracy of DCNN algorithm is improved from 78.1%to 80.3%,which verifies the feasibility and effectiveness of DCNN algorithm and feature interaction method for social characteristics.
作者
陈志毅
隋杰
CHEN Zhi-yi;SUI Jie(School of Engineering Science,University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《计算机科学》
CSCD
北大核心
2022年第1期101-107,共7页
Computer Science
基金
国家重点研发计划(2017YFB0803001)
国家自然科学基金(61572459)。
关键词
多模态
谣言检测
DeepFM
卷积神经网络
社会特征
自然语言处理
Multimodal
Rumor detection
DeepFM
Convolutional neural networks
Social feature
Natural language processing
作者简介
通讯作者:隋杰,born in 1996,postgra-duate.His main research interests include natural language processing and data mining.(suijie@ucas.ac.cn);陈志毅,born in 1976,associate professor.Her main research interests include data mining and social network analysis.18811722686@163.com。
