The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
False rumors detection on Sina Weibo by propagation structures
Abstract
This paper studies the problem of automatic detection of false rumors on Sina Weibo, the popular Chinese microblogging social network. Traditional feature-based approaches extract features from the false rumor message, its author, as well as the statistics of its responses to form a flat feature vector. This ignores the propagation structure of the messages and has not achieved very good results. We propose a graph-kernel based hybrid SVM classifier which captures the high-order propagation patterns in addition to semantic features such as topics and sentiments. The new model achieves a classification accuracy of 91.3% on randomly selected Weibo dataset, significantly higher than state-of-the-art approaches. Moreover, our approach can be applied at the early stage of rumor propagation and is 88% confident in detecting an average false rumor just 24 hours after the initial broadcast.
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LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
A note on two problems in connexion with graphs
Dijkstra, E. W. (1959) · Numerische Mathematik · doi:10.1007/bf01386390 · Cited by 24,043
(2021) · A First Course in Statistical Programming with R · doi:10.1017/9781108993456.011 · Cited by 13,724
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2002) · Advances in Neural Information Processing Systems 14 · doi:10.7551/mitpress/1120.003.0082 · Cited by 12,223
An argument for basic emotions
Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646
(1999) · Springer Series in Operations Research and Financial Engineering · doi:10.1007/b98874 · Cited by 9,482
Floyd, R. W. (1962) · Communications of the ACM · doi:10.1145/367766.368168 · Cited by 3,998
Information credibility on twitter
Castillo, C., Mendoza, M., & Poblete, B. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963500 · Cited by 2,593
Radial basis functions theory and implementations
Buhmann, M. (2003) · Cited by 1,861
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An overview of online fake news: Characterization, detection, and discussion
Zhang, X., & Ghorbani, A. A. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.004 · Cited by 997
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
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
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Metadata
- Authors
- Wu, K., Yang, S., & Zhu, K. Q.
- Venue
- 2015 IEEE 31st International Conference on Data Engineering
- Publisher
- IEEE
- Published
- 2015-04
- Pages
- 651-662
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W1546111015
- Cited by
- 585
- References
- 35
- FWCI
- 108.27
- Type
- conference paper
- Language
- en
- Open access
- closed
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