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基于卷积神经网络的谣言检测
引用本文:刘政,卫志华,张韧弦.基于卷积神经网络的谣言检测[J].计算机应用,2017,37(11):3053-3056.
作者姓名:刘政  卫志华  张韧弦
作者单位:1. 同济大学 计算机科学与技术系, 上海 201804;2. 嵌入式系统与服务计算教育部重点实验室(同济大学), 上海 201804
基金项目:国家自然科学基金资助项目(61573259,61673301,61573255,61673299);上海市中医药三年行动计划重点项目(ZY3-CCCX-3-6002);上海自然科学基金资助项目(15ZR1443800)。
摘    要:人工检测谣言通常需要耗费大量的人力物力,并且会有很长的检测延迟。目前现存的谣言检测模型一般根据谣言的内容、用户属性、传播方式人工地构造特征,而人工构建特征存在考虑片面、浪费人力等现象。为了解决这个问题,提出了基于卷积神经网络(CNN)的谣言检测模型。将微博中的谣言事件向量化,通过卷积神经网络隐含层的学习训练来挖掘表示文本深层的特征,避免了特征构建的问题,并能发现那些不容易被人发现的特征,从而产生更好的效果。实验结果表明,所提方法能够准确识别谣言事件,在准确率、精确率与F1值指标上优于支持向量机(SVM)与循环神经网络(RNN)等对比算法。

关 键 词:微博  谣言检测  谣言事件  卷积神经网络  
收稿时间:2017-05-16
修稿时间:2017-06-05

Rumor detection based on convolutional neural network
LIU Zheng,WEI Zhihua,ZHANG Renxian.Rumor detection based on convolutional neural network[J].journal of Computer Applications,2017,37(11):3053-3056.
Authors:LIU Zheng  WEI Zhihua  ZHANG Renxian
Affiliation:1. Department of Computer Science and Technology, Tongji University, Shanghai 201804, China;2. Key Laboratory of Embedded System and Service Computing of Ministry of Education(Tongji University), Shanghai 201804, China
Abstract:Manual rumor detection often consumes a lot of manpower and material resources, and there will be a long detection delay. At present, the existing rumor detection models construct features manually according to the content, user attributes, and pattern of the rumor transmission, which can not avoid one-sided consideration, waste of human and other phenomena. To solve this problem, a rumor detection model based on Convolutional Neural Network (CNN) was presented. The rumor events in microblog were vectorized. The deep features of text were mined through the learning and training in hidden layer of CNN to avoid the problem of feature construction, and those features that were not easily found could be found to produce better results. The experimental results show that the proposed method can accurately identify rumor events, and it is better than Support Vector Machine (SVM), Recurrent Neural Network (RNN) and other contrast algorithms in accuracy rate, precision rate and F1 score.
Keywords:microblog                                                                                                                        rumor detection                                                                                                                        rumor event                                                                                                                        Convolution Neural Network (CNN)
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