关键词: 谣言鉴别, 半监督学习, 图卷积神经网络

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.

Key words: rumor identification, semi-supervised learning, graph convolutional neural network