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山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (2): 102-106, 115.doi: 10.6040/j.issn.1672-3961.0.2018.189

• 机器学习与数据挖掘 • 上一篇    下一篇

基于C-GRU的微博谣言事件检测方法

李力钊1(),蔡国永1,潘角2   

  1. 1. 桂林电子科技大学计算机与信息安全学院, 广西 桂林 541004
    2. 桂林凯歌信息科技有限公司, 广西 桂林 541004
  • 收稿日期:2018-05-25 出版日期:2019-04-20 发布日期:2019-04-19
  • 作者简介:李力钊(1993—),男,山西长治人,硕士研究生,主要研究方向为数据挖掘,谣言检测.E-mail:786225251@qq.com
  • 基金资助:
    桂林市科学研究与技术开发计划项目(20170113-6)

A microblog rumor events detection method based on C-GRU

Lizhao LI1(),Guoyong CAI1,Jiao PAN2   

  1. 1. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China
    2. Guilin Kaige Information Technology Co., Ltd., Guilin 541004, Guangxi, China
  • Received:2018-05-25 Online:2019-04-20 Published:2019-04-19
  • Supported by:
    桂林市科学研究与技术开发计划项目(20170113-6)

摘要:

提出基于卷积-门控循环单元(convolution-gated recurrent unit, C-GRU)的微博谣言事件检测模型。结合卷积神经网络(convolutional neural networks, CNN)和门控循环单元(gated recurrent unit, GRU)的优点,将微博事件博文句向量化,通过CNN中的卷积层学习微博窗口的特征表示,将微博窗口特征按时间顺序拼接成窗口特征序列,将窗口特征序列输入GRU中学习序列特征表示进行谣言事件检测。在真实数据集上的试验结果表明,相比基于传统机器学习方法、CNN和GRU的谣言检测模型,该模型有更好的谣言识别能力。

关键词: 谣言事件检测, 深度学习, 卷积-门控循环单元, 窗口特征序列

Abstract:

A microblog rumor events detection model based on convolution-gated recurrent unit(C-GRU) was proposed. Combining the advantages of CNN and GRU, the microblog event′s posts was vectorized. By learning the features representation of the microblog windows through the convolution layer of CNN, the features of microblog windows was spliced into a sequence of window feature according to the time order, and the sequence of window feature was put into the GRU to learn feature representation of sequence for rumor events detection. Experimental results from real data sets showed that this model had better ability to rumor detection than other models based on traditional machine learning, CNN or RNN.

Key words: rumor events detection, deep learning, convolution-gated recurrent unit, window feature sequence

中图分类号: 

  • TP391.1

图1

基于C-GRU的谣言事件检测模型"

图2

卷积提取窗口特征"

图3

窗口特征拼接及窗口特征序列构建"

图4

GRU学习序列特征并输出结果"

表1

各方法准确率对比结果"

方法 Ac/%
SVM-RBF 79.75
DTC 81.25
RNN 87.25
1-LSTM 89.75
1-GRU 90.25
2-GRU 90.75
CNN 95.25
C-GRU 95.75

表2

准确率比较"

过滤器长度 滤器个数 Ac/%
2 180 92.50
3 180 93.25
4 180 94.25
5 180 93.75
2, 3 90 94.00
3, 4 90 94.50
4, 5 90 94.75
2, 3, 4 60 94.25
3, 4, 5 60 95.75
3, 4, 5 50 95.50
3, 4, 5 70 90.25
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