| 基于图卷积网络的谣言鉴别研究 |
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| 引用本文： | 米源,唐恒亮. 基于图卷积网络的谣言鉴别研究[J]. 计算机工程与应用, 2021, 57(13): 161-167. DOI: 10.3778/j.issn.1002-8331.2003-0357 |
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| 作者姓名： | 米源 唐恒亮 |
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| 作者单位： | 1.北京物资学院 信息学院,北京 1011492.北京工业大学 多媒体与智能软件技术北京市重点实验室,北京 100124 |
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| 摘 要： | 随着大数据时代的演进,互联网中的谣言成井喷状涌现.目前网络谣言鉴别方法中,基于监督学习的模型在训练过程中需要大量标注数据,同时网络谣言的人工标注用时较长,故提出采用半监督学习的图卷积神经网络,可有效利用无标注数据.通过在有标注节点上训练模型,更新所有节点共享的权重矩阵,将有标注节点信息传播给无标注节点,同时解决监督学习...
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| 关 键 词： | 谣言鉴别 半监督学习 图卷积神经网络 |
Rumor Identification Research Based on Graph Convolutional Network |
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| MI Yuan,TANG Hengliang. Rumor Identification Research Based on Graph Convolutional Network[J]. Computer Engineering and Applications, 2021, 57(13): 161-167. DOI: 10.3778/j.issn.1002-8331.2003-0357 |
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| Authors: | MI Yuan TANG Hengliang |
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| Affiliation: | 1.School of Information, Beijing Wuzi University, Beijing 101149, China2.Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing University of Technology, Beijing 100124, China |
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| Abstract: | With the evolution of the era of big data, rumors in the Internet have sprung up. In current rumor identification methods, supervised learning models require a large amount of labeled data during training, and the manual labeling of network rumors takes a long time. Therefore, this paper proposes a semi-supervised graph convolutional neural network, which can effectively use unlabeled data. The model trains on labeled nodes, updates the weight matrix shared by all nodes, and propagates the information of labeled nodes to unlabeled nodes. At the same time, it solves the problem of weak generalization of the supervised learning model and the instability of the unsupervised learning model. Compared with three network rumor identification methods based on SVM algorithm, logistic regression algorithm and BiLSTM model, respectively, the method in this paper reaches 86.1% and 85.3% in the two evaluation indicators of recall rate and F1 value, which further improves the accuracy and stability of network rumor identification. The method can effectively reduce the cost of manual tagging, identify rumors in social media and online news, and provide new ideas for the management of network rumors. |
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| Keywords: | rumor identification semi-supervised learning graph convolutional neural network |
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