2015 SEISMICASelfExcitingPointProces

• (Zhao et al., 2015) ⇒ Qingyuan Zhao, Murat A. Erdogdu, Hera Y. He, Anand Rajaraman, and Jure Leskovec. (2015). “SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783401

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Cited By

• http://scholar.google.com/scholar?q=%222015%22+SEISMIC%3A+A+Self-Exciting+Point+Process+Model+for+Predicting+Tweet+Popularity
• http://dl.acm.org/citation.cfm?id=2783258.2783401&preflayout=flat#citedby

Quotes

Author Keywords

• Cascade prediction; contagion; data mining; information diffusion; [[self-exciting point process; social media

Abstract

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?

We validate our model using one month of complete Twitter data and demonstrate a strong improvement in predictive accuracy over existing approaches. Our model gives only 15% relative error in predicting final size of an average information cascade after observing it for just one hour.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 SEISMICASelfExcitingPointProces	Anand Rajaraman Jure Leskovec Qingyuan Zhao Murat A. Erdogdu Hera Y. He			SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity				10.1145/2783258.2783401		2015