Abstract
Nowadays, social media has become a convenient and prevalent platform for users to communicate with others and share their opinions publicly. In the meantime, due to the rapid growth of social media, the circulation of untrue and irresponsible statements is also boosted, making it harder to detect rumors in the massive amount of social data. Existing deep learning-based approaches detect rumors by modeling the way they spread or their semantic features. However, most of them ignore the different levels of influence when various users participate in the spread of rumors. Hence, we define the influence power of users, which is related to the popularity of their posts, as influence factors, and users with higher influence factors are more likely to determine the direction of public opinion, which can also make rumors spread more quickly and widely. In this paper, we propose a novel graph model named Influence-based Bi-Directional Graph Convolutional Network (IBi-GCN) to capture the influence of users and the way a rumor spreads. First, our model uses an information entropy-based approach to calculate the local and global influence of users, respectively, and obtain the overall influence factors of users in the form of a weighted sum. Second, we combine the overall influence factor with the two main features of rumor propagation and diffusion. Finally, we use a bi-directional graph convolutional neural network to learn a high-level representation for rumor detection.
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References
Ahsan, M., Kumari, M., Sharma, T.: Rumors detection, verification and controlling mechanisms in online social networks: A survey. OSNM (2019)
Alahmadi, D.H., Zeng, X.J.: Ists: Implicit social trust and sentiment based approach to recommender systems. Expert Systems with Applications (2015)
Bian, T., Xiao, X., Xu, T.: 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 (2011)
Chen, X., Deng, L., Zhao, Y., Zhou, X., Zheng, K.: Community-based influence maximization in location-based social network. World Wide Web 24(6), 1903–1928 (2021). https://doi.org/10.1007/s11280-021-00935-x
DiFonzo, N., Bordia, P.: Rumor, gossip and urban legends. Diogenes (2007)
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)
Khoo, L.M.S., Chieu: Interpretable rumor detection in microblogs by attending to user interactions. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 8783–8790 (2020)
Kwon, S., Cha, M., Jung, K.: Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th international conference on data mining
Li, Q., Liu, X., Fang, R.: User behaviors in newsworthy rumors: A case study of twitter. In: Proceedings of the International AAAI Conference on Web and Social Media. pp. 627–630 (2016)
Lin, H., Ma, J., Cheng, M.: Rumor detection on twitter with claim-guided hierarchical graph attention networks. arXiv preprint arXiv:2110.04522 (2021)
Liu, G., Liu, Y., Zheng, K.: MCS-GPM: multi-constrained simulation based graph pattern matching in contextual social graphs. IEEE TKDE. pp. 1050–1064 (2018)
Liu, X., Nourbakhsh, A., Li, Q.: 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)
Ma, J., Gao, W.: Debunking rumors on twitter with tree transformer. ACL (2020)
Ma, J., Gao, W., Wei, Z.: 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)
Peng, S., Yang, A., Cao, L.: Social influence modeling using information theory in mobile social networks. Information Sciences 379, 146–159 (2017)
Peng, S., Zhou, Y., Cao, L.: Influence analysis in social networks: A survey. Journal of Network and Computer Applications 106, 17–32 (2018)
Popat, K.: Assessing the credibility of claims on the web. In: Proceedings of the 26th International Conference on World Wide Web Companion. pp. 735–739 (2017)
Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., Stein, B.: A stylometric inquiry into hyperpartisan and fake news. arXiv preprint arXiv:1702.05638 (2017)
Riquelme, F., González-Cantergiani, P.: Measuring user influence on twitter: A survey. Information processing & management 52(5), 949–975 (2016)
Rong, Y., Huang, W., Xu, T., Huang, J.: Dropedge: Towards deep graph convolutional networks on node classification. arXiv preprint arXiv:1907.10903 (2019)
Sampson, J., Morstatter, F., Wu, L.: Leveraging the implicit structure within social media for emergent rumor detection. In: Proceedings of the 25th ACM international on conference on information and knowledge management. pp. 2377–2382 (2016)
Thomas, S.A.: Lies, damn lies, and rumors: an analysis of collective efficacy, rumors, and fear in the wake of katrina. Sociological Spectrum 27(6), 679–703 (2007)
Yang, F., Liu, Y., Yu, X.: Automatic detection of rumor on sina weibo. In: Proceedings of the ACM SIGKDD workshop on mining data semantics. pp. 1–7 (2012)
Yu, F., Liu, Q., Wu, S., Wang, L., Tan, T., et al.: A convolutional approach for misinformation identification. In: IJCAI. pp. 3901–3907 (2017)
Acknowledgments
This work was supported by National Natural Science Foundation of China under grant (No. 61802273, 62102277), Postdoctoral Science Foundation of China (No. 2020M681529), Natural Science Foundation of Jiangsu Province (BK20210703), China Science and Technology Plan Project of Suzhou (No. SYG202139), Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX2\(\_\)11342), Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Chen, L., Fang, J., Chao, P., Liu, A., Zhao, P. (2022). Rumor Detection in Social Network via Influence Based on Bi-directional Graph Convolutional Network. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_20
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