A hierarchical model of non-homogeneous Poisson processes for Twitter retweets

Lee, Clement and Wilkinson, Darren J. (2019) A hierarchical model of non-homogeneous Poisson processes for Twitter retweets. Journal of the American Statistical Association, 115 (529). pp. 1-15. ISSN 0162-1459

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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

Item Type:
Journal Article
Journal or Publication Title:
Journal of the American Statistical Association
Additional Information:
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 30/04/2019, available online: https://www.tandfonline.com/doi/full/10.1080/01621459.2019.1585358
Uncontrolled Keywords:
?? bayesian methodsmarkov chain monte carlomodel selectionstochastic processesstatistics and probabilitystatistics, probability and uncertainty ??
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Deposited On:
22 Jun 2019 09:10
Last Modified:
15 Jul 2024 19:22