基于C-GRU的微博谣言事件检测方法

doi:10.6040/j.issn.1672-3961.0.2018.189

摘要/Abstract

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

李力钊, 蔡国永, 潘角. 基于C-GRU的微博谣言事件检测方法[J]. 山东大学学报 (工学版), 2019, 49(2): 102-106.

Lizhao LI, Guoyong CAI, Jiao PAN. A microblog rumor events detection method based on C-GRU[J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 102-106.

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方法	A_c/%
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

过滤器长度	滤器个数	A_c/%
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