Abstract
Online social media platforms have been developing rapidly in the era of the Internet and big data, which accelerate rumors being circulated. The spread of rumors might damage citizen rights and disturb social stability. Rumor detection on social media is a challenging task worldwide due to rumor’s feature of the high speed, fragmental information, and extensive range. In this paper, we propose a novel model for rumor detection based on Graph Neural Networks (GNN), named Dual-grained Feature Aggregation Graph Neural Networks (Du-FAGNN). It applies a Graph Convolutional Network (GCN) with a graph of rumor propagation to learn the text-granularity representations with the spreading of events. We employ a GNN with a document graph to update aggregated features of both word and text granularity, it helps to form final representations of events to detect rumors. Experiments on the Sina Weibo dataset validate the performance of the proposed method for rumor detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The Code of our Du-FAGNN model is available and can be accessed via: https://github.com/LXD789/Du-FAGNN.
References
Benamira, A., Devillers, B., Lesot, E., Ray, A.K., Saadi, M., Malliaros, F.D.: Semi-supervised learning and graph neural networks for fake news detection. In: ASONAM ’19: International Conference on Advances in Social Networks Analysis and Mining (2019)
Bian, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 549–556 (2020)
Castillo, C., Mendoza, M., Poblete, B.: Information credibility on twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 675–684 (2011)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Dong, M., Zheng, B., Quoc Viet Hung, N., Su, H., Li, G.: Multiple rumor source detection with graph convolutional networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 569–578 (2019)
Gao, J., Han, S., Song, X., Ciravegna, F.: Rp-dnn: A tweet level propagation context based deep neural networks for early rumor detection in social media. arXiv preprint arXiv:2002.12683 (2020)
Han, Y., Karunasekera, S., Leckie, C.: Graph neural networks with continual learning for fake news detection from social media. arXiv preprint arXiv:2007.03316 (2020)
Ke, Z., Li, Z., Zhou, C., Sheng, J., Silamu, W., Guo, Q.: Rumor detection on social media via fused semantic information and a propagation heterogeneous graph. Symmetry 12(11), 1806 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Kumar, S., Carley, K.M.: Tree lstms with convolution units to predict stance and rumor veracity in social media conversations. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5047–5058 (2019)
Liu, X., Nourbakhsh, A., Li, Q., Fang, R., Shah, S.: Real-time rumor debunking on twitter. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1867–1870 (2015)
Liu, Y., Wu, Y.F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Lu, Y.J., Li, C.T.: Gcan: Graph-aware co-attention networks for explainable fake news detection on social media. arXiv preprint arXiv:2004.11648 (2020)
Lukasik, M., Cohn, T., Bontcheva, K.: Classifying tweet level judgements of rumours in social media. In: Conference on Empirical Methods in Natural Language Processing (2015)
Ma, J., et al.: Detecting rumors from microblogs with recurrent neural networks (2016)
Ma, J., Gao, W., Wei, Z., Lu, Y., Wong, K.F.: 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, pp. 1751–1754 (2015)
Ma, J., Gao, W., Wong, K.F.: Detect rumors in microblog posts using propagation structure via kernel learning. Association for Computational Linguistics (2017)
Ma, J., Gao, W., Wong, K.F.: Rumor detection on twitter with tree-structured recursive neural networks. Association for Computational Linguistics (2018)
Ruchansky, N., Seo, S., Liu, Y.: Csi: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 797–806 (2017)
Sejeong, K., Meeyoung, C., Kyomin, J.: Rumor detection over varying time windows. Plos One 12(1), e0168344 (2017)
Shu, K., Wang, S., Liu, H.: Beyond news contents: the role of social context for fake news detection. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 312–320 (2019)
Wu, K., Yang, S., Zhu, K.Q.: False rumors detection on sina weibo by propagation structures. In: 2015 IEEE 31st International Conference on Data Engineering, pp. 651–662 (2015). https://doi.org/10.1109/ICDE.2015.7113322
Wu, L., Liu, H.: Tracing fake-news footprints: characterizing social media messages by how they propagate. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 637–645 (2018)
Wu, Z., Pi, D., Chen, J., Xie, M., Cao, J.: Rumor detection based on propagation graph neural network with attention mechanism. Expert Syst. Appl. 158, 113595 (2020)
Yang, F., Liu, Y., Yu, X., Yang, M.: Automatic detection of rumor on sina weibo. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, pp. 1–7 (2012)
Zhang, Y., Yu, X., Cui, Z., Wu, S., Wen, Z., Wang, L.: Every document owns its structure: inductive text classification via graph neural networks. arXiv preprint arXiv:2004.13826 (2020)
Zhao, Z., Resnick, P., Mei, Q.: Enquiring minds: early detection of rumors in social media from enquiry posts. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1395–1405 (2015)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (Grant No. U1703261 ). The corresponding author is Kai Ma.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, S., Liu, X., Ma, K., Dong, F., Xiang, S., Bing, C. (2021). Rumor Detection on Microblogs Using Dual-Grained Feature via Graph Neural Networks. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13032. Springer, Cham. https://doi.org/10.1007/978-3-030-89363-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-89363-7_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89362-0
Online ISBN: 978-3-030-89363-7
eBook Packages: Computer ScienceComputer Science (R0)