A Novel and High-Accuracy Rumor Detection Approach using Kernel Subtree and Deep Learning Networks

Wei, Ziyu1; Xiao, Xi1,2; Hu, Guangwu3; Zhang, Bin2; Li, Qing4; Xia, Shutao1,2

10.1109/IJCNN52387.2021.9534311

2021

International Joint Conference on Neural Networks (IJCNN)

2161-4393

978-1-6654-4597-9

2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)

1-8

JUL 18-22, 2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Rumor detection is a task of identifying information that spread among people whose truth value is false or unverified, and it has been a great challenge due to the rapid development of social media. The traditional machine learning based detection methods can make full use of informative features but cannot extract high-level representations. Other methods involved deep learning neural networks exploit propagation structural information to achieve high accuracy, for example, Bi-Directional Graph Convolution Networks(BiGCN) achieved the best performance on rumor detection by operating on bottom-up and top-down structures. However, those deep learning methods ignore other useful features like content-based features. In this paper, we not only make full use of three aspects of features based on a new concept: kernel subtree, which focus more on informative features of influential nodes of an event, but also propose a new model, which consists of Separation Convolution blocks, Long Short Term Memory(LSTM) and Squeeze and Excitation Networks(SENet), to make comprehensive use of features extracted on the basis of kernel subtree. First, we utilize Separation Convolutions to learn more local information with different kernel size, then LSTM can learn high-level interactions among features and find more global information. After that, SENet applies attention mechanism to put more weights on informative channels of feature maps. Meanwhile, on test set, Gradient Boosting Decision Tree(GBDT) is used to assist our model with few events. The experiments on the PHEME dataset show that our approach can identify rumors with accuracy 95% which outperforms BiGCN by 10% at least.

Deep learning Convolution Social networking (online) Neural networks Feature extraction Boosting Decision trees

其他

英语

CPCI-S ; EI

National Key Research and Development Program of China["2018YFB1800204","2018YFB1800601"] ; National Natural Science Foundation of China[61972219] ; RD Program of Shenzhen["JCYJ20190813174403598","SGDX20190918101201696"] ; National Natural Science Foundation of Guangdong Province[2018A030313422]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic

WOS:000722581708006

Web of Science

1.Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Beijing, Peoples R China
2.Peng Cheng Lab, Beijing, Peoples R China
3.Shenzhen Inst Informat Technol, Shenzhen, Peoples R China
4.Southern Univ Sci & Technol, Shenzhen, Peoples R China

Wei, Ziyu,Xiao, Xi,Hu, Guangwu,et al. A Novel and High-Accuracy Rumor Detection Approach using Kernel Subtree and Deep Learning Networks[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:1-8.