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    Rights statement: 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

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

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E-pub ahead of print
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<mark>Journal publication date</mark>30/04/2019
<mark>Journal</mark>Journal of the American Statistical Association
Issue number529
Volume115
Number of pages15
Pages (from-to)1-15
Publication statusE-pub ahead of print
Early online date30/04/19
Original languageEnglish

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

Bibliographic note

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