Peng Bao
ABSTRACT
The ability to model and predict the popularity dynamics of individual user generated items on online media has important implications in a wide range of areas. In this paper, we propose a probabilistic model using a Self-Excited Hawkes Process (SEHP) to characterize the process through which individual microblogs gain their popularity. This model explicitly captures the triggering effect of each forwarding, distinguishing itself from the reinforced Poisson process based model where all previous forwardings are simply aggregated as a single triggering effect. We validate the proposed model by applying it on Sina Weibo, the most popular microblogging network in China. Experimental results demonstrate that the SEHP model consistently outperforms the model based on reinforced Poisson process.
- P. Bao, H. W. Shen, J. Huang, X. Q. Cheng. Popularity Prediction in Microblogging Network: a Case Study on Sina Weibo. In Proc. of WWW '13, pp. 177--178, Brazil. Google Scholar
Digital Library
- R. Crane, D. Sornette. Robust dynamic classes revealed by measuring the response function of a social system. Proc. Natl. Acad. Sci., 105(41): 15649--15653, 2008.Google ScholarCross Ref
- M. Gomez-Rodriguez, J. Leskovec, B. Scholkopf. Modeling Information Propagation with Survival Theory. In Proc. of ICML '13, pp. 666--674, USA.Google Scholar
- H. W. Shen, D. Wang, C. Song, A.-L. Barabasi. Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes. In Proc. of AAAI '14, pp. 291--297, Canada.Google ScholarDigital Library
- G. Szabo, B. A. Huberman. Predicting the popularity of online content. Commun. ACM, 53(8): 80--88, 2010. Google ScholarDigital Library
Index Terms
- Modeling and Predicting Popularity Dynamics of Microblogs using Self-Excited Hawkes Processes
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