The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Epidemiological modeling of news and rumors on Twitter
Abstract
Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.
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Cites
What is Twitter, a social network or a news media?
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Spread of epidemic disease on networks
Newman, M. E. J. (2002) · Physical Review E · doi:10.1103/physreve.66.016128 · Cited by 3,218
Measuring user influence in twitter: The million follower fallacy
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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
Mathematical Biology: I. An Introduction
(2002) · Interdisciplinary Applied Mathematics · doi:10.1007/b98868 · Cited by 1,763
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Twitter under crisis: can we trust what we RT?
Mendoza, M., Poblete, B., & Castillo, C. (2010) · Proceedings of the First Workshop on Social Media Analytics · doi:10.1145/1964858.1964869 · Cited by 955
DALEY, D. J., & KENDALL, D. G. (1964) · Nature · doi:10.1038/2041118a0 · Cited by 872
Limiting the spread of misinformation in social networks
Budak, C., Agrawal, D., & El Abbadi, A. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963499 · Cited by 807
Theory of rumour spreading in complex social networks
Nekovee, M., Moreno, Y., Bianconi, G., & Marsili, M. (2007) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2006.07.017 · Cited by 796
Cited by
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847
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
Liu, Y., & Wu, Y.-F. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11268 · Cited by 663
“(Jin et al. 2013) utilize epidemiological models to characterize information cascades in Twitter resulting from both true news and fake news.”
BackgroundFalse 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
Metadata
- Authors
- Jin, F., Dougherty, E., Saraf, P., Cao, Y., & Ramakrishnan, N.
- Venue
- Proceedings of the 7th Workshop on Social Network Mining and Analysis
- Publisher
- ACM
- Published
- 2013-08-11
- Pages
- 1-9
- OpenAlex
- W2164082612
- Cited by
- 342
- References
- 24
- FWCI
- 51.07
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Jin, F., Dougherty, E., Saraf, P., Cao, Y., & Ramakrishnan, N. (2013). Epidemiological modeling of news and rumors on Twitter. Proceedings of the 7th Workshop on Social Network Mining and Analysis. https://doi.org/10.1145/2501025.2501027
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
