A Hierarchical Model of Nonhomogeneous Poisson Processes for Twitter Retweets

Abstract

We present a hierarchical model of nonhomogeneous Poisson processes (NHPP) for information diffusion on online social media, in particular Twitter retweets. The retweets of each original tweet are modelled by a NHPP, for which the intensity function is a product of time-decaying components and another component that depends on the follower count of the original tweet author. The latter allows us to explain or predict the ultimate retweet count by a network centrality-related covariate. The inference algorithm enables the Bayes factor to be computed, to facilitate model selection. Finally, the model is applied to the retweet datasets of two hashtags. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement

Keywords:

• Bayesian methods
• Markov chain Monte Carlo
• Model selection
• Stochastic processes

Acknowledgments

Data supporting this publication is openly available under an “Open Data Commons Open Database License.” Additional metadata are available at: http://dx.doi.org/10.17634/154300-57. Please contact Newcastle Research Data Service at [email protected] for access instructions. This research was funded by the Engineering and Physical Sciences Research Council (EPSRC) grant DERC: Digital Economy Research Centre (EP/M023001/1).