The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Detect Rumors Using Time Series of Social Context Information on Microblogging Websites
Open accessAbstract
Automatically identifying rumors from online social media especially microblogging websites is an important research issue. Most of existing work for rumor detection focuses on modeling features related to microblog contents, users and propagation patterns, but ignore the importance of the variation of these social context features during the message propagation over time. In this study, we propose a novel approach to capture the temporal characteristics of these features based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information. Our experiments using the events in two microblog datasets confirm that the method outperforms state-of-the-art rumor detection approaches by large margins. Moreover, our model demonstrates strong performance on detecting rumors at early stage after their initial broadcast.
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Cites
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Prominent Features of Rumor Propagation in Online Social Media
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TwitterMonitor: trend detection over the twitter stream
Mathioudakis, M., & Koudas, N. (2010) · Proceedings of the 2010 ACM SIGMOD International Conference on Management of data · doi:10.1145/1807167.1807306 · Cited by 813
(2011) · Cited by 724
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
False rumors detection on Sina Weibo by propagation structures
Wu, K., Yang, S., & Zhu, K. Q. (2015) · 2015 IEEE 31st International Conference on Data Engineering · doi:10.1109/icde.2015.7113322 · Cited by 585
Ratkiewicz (2011) · doi:10.1145/1963192.1963301 · Cited by 541
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Cited by
Fake News Detection on Social Media: A Data Mining Perspective
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3137597.3137600 · Cited by 3,304
CSI: A Hybrid Deep Model for Fake News Detection
Ruchansky, N., Seo, S., & Liu, Y. (2017) · Proceedings of the 2017 ACM on Conference on Information and Knowledge Management · doi:10.1145/3132847.3132877 · Cited by 1,039
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
Detection and Resolution of Rumours in Social Media: A Survey
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2019) · ACM Computing Surveys · doi:10.1145/3161603 · Cited by 862
“[Ma et al. 2015] proposed to model features over time.”
BackgroundRumor 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
Metadata
- Authors
- Ma, J., Gao, W., Wei, Z., Lu, Y., & Wong, K.-F.
- Venue
- Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2015-10-17
- Pages
- 1751-1754
- OpenAlex
- W1975594555
- Cited by
- 640
- References
- 11
- FWCI
- 104.62
- Type
- conference paper
- Language
- en
- Open access
- green
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- Ma, J., Gao, W., Wei, Z., Lu, Y., & Wong, K.-F. (2015). Detect Rumors Using Time Series of Social Context Information on Microblogging Websites. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. https://doi.org/10.1145/2806416.2806607
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