The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning
Open accessAbstract
How fake news goes viral via social media?How does its propagation pattern differ from real stories?In this paper, we attempt to address the problem of identifying rumors, i.e., fake information, out of microblog posts based on their propagation structure.We firstly model microblog posts diffusion with propagation trees, which provide valuable clues on how an original message is transmitted and developed over time.We then propose a kernel-based method called Propagation Tree Kernel, which captures high-order patterns differentiating different types of rumors by evaluating the similarities between their propagation tree structures.Experimental results on two real-world datasets demonstrate that the proposed kernel-based approach can detect rumors more quickly and accurately than state-ofthe-art rumor detection models.
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Cites
Automatic detection of rumor on Sina Weibo
Yang, F., Liu, Y., Yu, X., & Yang, M. (2012) · Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics · doi:10.1145/2350190.2350203 · Cited by 680
Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts
Zhao, Z., Resnick, P., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741637 · Cited by 583
Real-time Rumor Debunking on Twitter
Liu, X., Nourbakhsh, A., Li, Q., Fang, R., & Shah, S. (2015) · Proceedings of the 24th ACM International on Conference on Information and Knowledge Management · doi:10.1145/2806416.2806651 · Cited by 391 · cited 5×
“Following that, various features corresponding to message contents, user profiles and statistics of propagation patterns were proposed in many studies (Yang et al., 2012; Wu et al., 2015; Sun et al., 2013; Liu et al., 2015).”
InfluentialRumor Detection over Varying Time Windows
Kwon, S., Cha, M., & Jung, K. (2017) · PLOS ONE · doi:10.1371/journal.pone.0168344 · Cited by 352
Hannak, A., Margolin, D., Keegan, B., & Weber, I. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14555 · Cited by 90
Cited by
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks
Bian, T., Xiao, X., Xu, T., Zhao, P., Huang, W., Rong, Y., & Huang, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5393 · Cited by 796
A Stylometric Inquiry into Hyperpartisan and Fake News
Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., & Stein, B. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1022 · Cited by 686
Liu, Y., & Wu, Y.-F. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11268 · Cited by 663
A survey on fake news and rumour detection techniques
Bondielli, A., & Marcelloni, F. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.035 · Cited by 658
Combating Fake News: A Survey on Identification and Mitigation Techniques
Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., & Liu, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3305260 · Cited by 514
Metadata
- Authors
- Ma, J., Gao, W., & Wong, K.-F.
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 708-717
- OpenAlex
- W2742144412
- Cited by
- 637
- References
- 5
- FWCI
- 129.05
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Ma, J., Gao, W., & Wong, K.-F. (2017). Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p17-1066
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