The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity
Open accessAbstract
Social networking websites allow users to create and share content. Big information cascades of post resharing can form as users of these sites reshare others' posts with their friends and followers. One of the central challenges in understanding such cascading behaviors is in forecasting information outbreaks, where a single post becomes widely popular by being reshared by many users. In this paper, we focus on predicting the final number of reshares of a given post. We build on the theory of self-exciting point processes to develop a statistical model that allows us to make accurate predictions. Our model requires no training or expensive feature engineering. It results in a simple and efficiently computable formula that allows us to answer questions, in real-time, such as: Given a post's resharing history so far, what is our current estimate of its final number of reshares? Is the post resharing cascade past the initial stage of explosive growth? And, which posts will be the most reshared in the future?
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Cites
Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes
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Spectra of some self-exciting and mutually exciting point processes
HAWKES, A. G. (1971) · Biometrika · doi:10.1093/biomet/58.1.83 · Cited by 2,252
The origin of bursts and heavy tails in human dynamics
Barabási, A.-L. (2005) · Nature · doi:10.1038/nature03459 · Cited by 2,210
Everyone's an influencer: quantifying influence on twitter
Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935845 · Cited by 1,689
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network
Suh, B., Hong, L., Pirolli, P., & Chi, E. H. (2010) · 2010 IEEE Second International Conference on Social Computing · doi:10.1109/socialcom.2010.33 · Cited by 1,166
Patterns of temporal variation in online media
Yang, J., & Leskovec, J. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935863 · Cited by 986
Self-citationPredicting the popularity of online content
Szabo, G., & Huberman, B. A. (2010) · Communications of the ACM · doi:10.1145/1787234.1787254 · Cited by 983
Self-Exciting Point Process Modeling of Crime
Mohler, G. O., Short, M. B., Brantingham, P. J., Schoenberg, F. P., & Tita, G. E. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.ap09546 · Cited by 892
Robust dynamic classes revealed by measuring the response function of a social system
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Predicting popular messages in Twitter
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Cited by
A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances
Zhou, F., Xu, X., Trajcevski, G., & Zhang, K. (2022) · ACM Computing Surveys · doi:10.1145/3433000 · Cited by 259
Fake News Detection in Social Networks via Crowd Signals
Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., & Krause, A. (2018) · Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18 · doi:10.1145/3184558.3188722 · Cited by 229
The Virality of Hate Speech on Social Media
Maarouf, A., Pröllochs, N., & Feuerriegel, S. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3641025 · Cited by 35
Metadata
- Authors
- Zhao, Q., Erdogdu, M. A., He, H. Y., Rajaraman, A., & Leskovec, J.
- Venue
- Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2015-08-10
- Pages
- 1513-1522
- OpenAlex
- W2949377321
- Cited by
- 478
- References
- 20
- FWCI
- 51.18
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhao, Q., Erdogdu, M. A., He, H. Y., Rajaraman, A., & Leskovec, J. (2015). SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2783258.2783401
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