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。
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 microhlog 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.
出处
《计算机应用》
CSCD
北大核心
2017年第11期3053-3056,3100,共5页
journal of Computer Applications
基金
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)~~
关键词
微博
谣言检测
谣言事件
卷积神经网络
microblog
rumor detection
rumor event
Convolution Neural Network （CNN）
