Cited By
View all- Yuan YPang NZhang YLiu K(2023)Which cascade is more decisive in rumor detection on social mediaKnowledge-Based Systems10.1016/j.knosys.2023.110857278:COnline publication date: 25-Oct-2023
GCNRDM: : A Social Network Rumor Detection Method Based on Graph Convolutional Network in Mobile Computing
Authors: 
Dawei Xu, Qing Liu, Liehuang Zhu, Zhonghua Tan, Feng Gao, Jian Zhao Academic Editor: Lihua YinAuthors Info & Claims
Abstract
Mobile computing is a new technology emerging with the development of mobile communication, Internet, database, distributed computing, and other technologies. Mobile computing technology will enable computers or other information intelligent terminal devices to realize data transmission and resource sharing in the wireless environment. Its role is to bring useful, accurate, and timely information to any customer at anytime, anywhere, and to change the way people live and work. In mobile computing environment, a lot of Internet rumors hidden among the huge amounts of information communication network can cause harm to society and people’s life; this paper proposes a model of social network rumor detection based on convolution networks, the use of adjacency matrix between the nodes represent user and the relationship between the constructions of social network topology. We use a high-order graph neural network (K-GNN) to extract the rumor posting features. At the same time, the graph attention network (GAT) is used to extract the association features of other nodes of the network topology. The experimental results show that the method of the detection model in this paper improves the accuracy of prediction classification compared with deep learning methods such as RNN, GRU, and attention mechanism. The innovation of the paper proposes a rumor detection model based on the graph convolutional network, which lies in considering the propagation structure among users. It has a strong practical value.
References
[1]
Q. Wu, F. C. Chen, R. Y. Huang, and C. Z. Chao, “A semantic path-based approach to heterogeneous network community discovery,” Journal of Electronics, vol. 44, no. 6, pp. 1465–1471, 2016.
[2]
L. Wu, J. Li, X. Hu, and H. Liu, “Gleaning wisdom from the past: early detection of emerging rumors in social media,” in Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 99–107, Houston, Texas, USA, 2017.
[3]
J. Ma, W. Gao, P. Mitra, S. Kwon, and M. Cha, “Detecting rumors from microblogs with recurrent neural networks,” in International Joint Conference on Artificial Intelligence, New York, USA, 2016.
[4]
C. Morris, M. Ritzert, M. Fey, W. L. Hamilton, and M. Grohe, “Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4602–4609, 2019.
[5]
T. Bian, X. Xiao, T. Xu, P. Zhao, W. Huang, Y. Rong, and J. Huang, “Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 1, pp. 549–556, 2020.
[6]
Y. Rong, W. Huang, T. Xu, and J. Huang, DropEdge: Towards Deep Graph Convolutional Networks on Node Classification, 8th International Conference on Learning Representations, ICLR, Addis Ababa, Ethiopia, 2020.
[7]
M. Mendoza, B. Poblete, and C. Castillo, “Twitter under crisis can we trust what we RT?” in Proceedings of the First Workshop on Social Media Analytics, pp. 71–79, Washington, DC, USA, 2010.
[8]
F. Yang, Y. Liu, X. Yu, and M. Yang, “Automatic detection of rumor on Sina Weibo,” in Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics - MDS '12, New York, USA, 2012.
[9]
S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang, “Prominent features of rumor propagation in online social media,” in 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, December 2013.
[10]
X. Liu, A. Nourbakhsh, Q. Li, R. Fang, and S. Shah, “Real-time rumor debunking on twitter,” in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, New York, USA, October 2015.
[11]
Z. Zhao, P. Resnick, and Q. Mei, “Enquiring minds: early detection of rumors in social media from enquiry posts,” in Proceedings of the 24th International Conference on World Wide Web, Florida, Italy, May 2015.
[12]
M. Jing, G. Wei, Z. Wei, Y. Lu, and K.-F. Wong, “Detect rumors using time series of social context information on microblogging websites,” in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, New York, USA, October 2015.
[13]
L. Yin, X. Luo, C. Zhu, L. Wang, Z. Xu, and H. Lu, “ConnSpoiler: disrupting C&C communication of IoT-based botnet through fast detection of anomalous domain queries,” IEEE Transactions on Industrial Informatics, vol. 16, no. 2, pp. 1373–1384, 2020.
[14]
J. Ma, W. Gao, and K.-F. Wong, “Detect rumors in microblog posts using propagation structure via kernel learning,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 708–717, Vancouver, Canada, 2017.
[15]
F. Yu, Q. Liu, S. Wu, L. Wang, and T. Tan, “A convolutional approach for misinformation identification,” in Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 3901–3907, AAAI Press, 2017.
[16]
C. Tong, L. Xue, H. Yin, and J. Zhang, “Call attention to rumors: deep attention based recurrent neural networks for early rumor detection,” in Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Cham, 2018.
[17]
S. Su, Z. Tian, S. Li, J. Deng, L. Yin, X. du, and M. Guizani, “IoT root union: a decentralized name resolving system for IoT based on blockchain,” Information Processing & Management, vol. 58, no. 3, article 102553, 2021.
[18]
W. Ke, Y. Song, and K. Q. Zhu, “False rumors detection on Sina Weibo by propagation structures,” in 2015 IEEE 31st International Conference on Data Engineering, Seoul, Korea (South), April 2015.
[19]
J. Ma, W. Gao, and K.-F. Wong, “Detect rumors on twitter by promoting information campaigns with generative adversarial learning,” in The World Wide Web Conference on - WWW '19, pp. 3049–3055, San Francisco, USA, 2019.
[20]
J. Ma, W. Gao, and K.-F. Wong, “Rumor detection on twitter with tree-structured recursive neural networks,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1980–1989, Melbourne, Australia, July 2018.
[21]
S. Su, Z. Tian, S. Liang, S. Li, S. du, and N. Guizani, “A reputation management scheme for efficient malicious vehicle identification over 5G networks,” IEEE Wireless Communications, vol. 27, no. 3, pp. 46–52, 2020.
[22]
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” Advances in Neural Information Processing Systems, vol. 29, pp. 3844–3852, 2016.
[23]
N. T. Kipf and M. Welling, “Semi supervised classification with graph convolutional networks,” in Proceedings of the International Conference on Learning Representations, Toulon, France, 2017.
[24]
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” 2017, https://arxiv.org/abs/1710.10903.
[25]
J. Qiu, Y. Chai, Z. Tian, X. du, and M. Guizani, “Automatic concept extraction based on semantic graphs from big data in smart city,” IEEE Transactions on Computational Social Systems, vol. 7, no. 1, pp. 225–233, 2020.
[26]
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in International conference on machine learning, Sydney, Australia, 2017.
[27]
Y. Rong, W. Huang, T. Xu, and H. Junzhou, “The truly deep graph convolutional networks for node classification,” 2019, https://arxiv.org/abs/1907.10903.
[28]
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929–1958, 2014.
[29]
“thunlp/Chinese_Rumor_Dataset [EB/OL],” 2019, https://github.com/thunlp/Chinese_Rumor_Dataset.
[30]
D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” 2014, https://arxiv.org/abs/1412.6980.
[31]
D. Xu, Z. Tian, R. Lai, X. Kong, Z. Tan, and W. Shi, “Deep learning based emotion analysis of microblog texts,” Information Fusion, vol. 64, pp. 1–11, 2020.
[32]
D. Mudali, L. K. Teune, R. J. Renken, K. L. Leenders, and J. B. T. M. Roerdink, “Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features,” Computational and Mathematical Methods in Medicine, vol. 2015, 2015.
[33]
H. Bisgin, O. U. Kilinc, A. Ugur, X. Xu, and V. Tuzcu, “Diagnosis of long QT syndrome via support vector machines classification,” Journal of Biomedical Science and Engineering, vol. 4, no. 4, pp. 264–271, 2011.
[34]
H. Ming, Y. Lu, Z. Zhang, and M. Dong, “A light-weight method of building an LSTM-RNN-based bilingual tts system,” in 2017 International Conference on Asian Language Processing (IALP), Singapore, December 2017.
[35]
A. Wu, D. Pi, J. Chen, M. Xie, and J. Cao, “Rumor detection based on propagation graph neural network with attention mechanism,” Expert Systems with Applications, vol. 158, article 113595, 2020.
[36]
L. Yang and Y. Wu, “Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks,” in Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, 2018.
[37]
F. Xing and C. Guo, “Mining semantic information in rumor detection via a deep visual perception based recurrent neural networks,” in 2019 IEEE International Congress on Big Data (BigDataCongress), Milan, Italy, July 2019.
[38]
S. Y. Louis, Y. Zhao, A. Nasiri, X. Wang, Y. Song, F. Liu, and J. Hu, “Graph convolutional neural networks with global attention for improved materials property prediction,” Physical Chemistry Chemical Physics, vol. 22, no. 32, pp. 18141–18148, 2020.
[39]
J. Bruna, W. Zaremba, A. Szlam, and Y. Lecun, “Spectral Networks and Locally Connected Networks on Graphs, Computer Science,” 2013, http://arxiv.org/abs/1312.6203.
Index Terms
- GCNRDM: A Social Network Rumor Detection Method Based on Graph Convolutional Network in Mobile Computing
Index terms have been assigned to the content through auto-classification.
Recommendations
UbiPhone: Human-Centered Ubiquitous Phone System
Emerging rich wireless networking modalities facilitate the development of new intelligent, innovative services on smart phones. The authors propose a ubiquitous phone (UbiPhone) system that demonstrates innovative context-aware human-centric phone ...
Special track on Ubiquitous Computing: Ubiquitous and Pervasive eCommerce and eBusiness: editorial message
SAC '08: Proceedings of the 2008 ACM symposium on Applied computingUbiquitous computing has emerged as one of the principal information and communication technologies that could lead to the achievement of the Ambient Intelligence (AmI) vision. Ambient Intelligence places human beings at the centre of the future ...
Identifying the influential bloggers in a community
WSDM '08: Proceedings of the 2008 International Conference on Web Search and Data MiningBlogging becomes a popular way for a Web user to publish information on the Web. Bloggers write blog posts, share their likes and dislikes, voice their opinions, provide suggestions, report news, and form groups in Blogosphere. Bloggers form their ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2021 Dawei Xu et al.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher
John Wiley and Sons Ltd.
United Kingdom
Publication History
Published: 01 January 2021
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 20 Jan 2025
Other Metrics
Citations
Cited By
View all- Yuan YPang NZhang YLiu K(2023)Which cascade is more decisive in rumor detection on social mediaKnowledge-Based Systems10.1016/j.knosys.2023.110857278:COnline publication date: 25-Oct-2023