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

Lookup NU author(s): Dr Clement LeeORCiD, Professor Darren Wilkinson



This is the authors' accepted manuscript of an article that has been published in its final definitive form by Taylor & Francis, 2020.

For re-use rights please refer to the publisher's terms and conditions.


We present a hierarchical model of non-homogeneous 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, in order to facilitate model selection. Finally, the model is applied to the retweet data sets of two hashtags.

Publication metadata

Author(s): Lee C, Wilkinson DJ

Publication type: Article

Publication status: Published

Journal: Journal of the American Statistical Association

Year: 2020

Volume: 115

Issue: 529

Pages: 1-15

Online publication date: 09/03/2019

Acceptance date: 16/02/2019

Date deposited: 09/03/2019

ISSN (print): 0162-1459

ISSN (electronic): 1537-274X

Publisher: Taylor & Francis


DOI: 10.1080/01621459.2019.1585358


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