Home > Research > Publications & Outputs > A hierarchical model of non-homogeneous Poisson...

Electronic data

  • accepted_20190309

    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

    Accepted author manuscript, 1.31 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

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

Research output: Contribution to journalJournal article

E-pub ahead of print

Standard

A hierarchical model of non-homogeneous Poisson processes for Twitter retweets. / Lee, Clement; Wilkinson, Darren J.

In: Journal of the American Statistical Association, Vol. 115, No. 529, 30.04.2019, p. 1-15.

Research output: Contribution to journalJournal article

Harvard

Lee, C & Wilkinson, DJ 2019, 'A hierarchical model of non-homogeneous Poisson processes for Twitter retweets', Journal of the American Statistical Association, vol. 115, no. 529, pp. 1-15. https://doi.org/10.1080/01621459.2019.1585358

APA

Lee, C., & Wilkinson, D. J. (2019). A hierarchical model of non-homogeneous Poisson processes for Twitter retweets. Journal of the American Statistical Association, 115(529), 1-15. https://doi.org/10.1080/01621459.2019.1585358

Vancouver

Lee C, Wilkinson DJ. A hierarchical model of non-homogeneous Poisson processes for Twitter retweets. Journal of the American Statistical Association. 2019 Apr 30;115(529):1-15. https://doi.org/10.1080/01621459.2019.1585358

Author

Lee, Clement ; Wilkinson, Darren J. / A hierarchical model of non-homogeneous Poisson processes for Twitter retweets. In: Journal of the American Statistical Association. 2019 ; Vol. 115, No. 529. pp. 1-15.

Bibtex

@article{59763d654d134649923c75a11439411a,
title = "A hierarchical model of non-homogeneous 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",
author = "Clement Lee and Wilkinson, {Darren J.}",
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",
year = "2019",
month = apr
day = "30",
doi = "10.1080/01621459.2019.1585358",
language = "English",
volume = "115",
pages = "1--15",
journal = "Journal of the American Statistical Association",
issn = "0162-1459",
publisher = "Taylor and Francis Ltd.",
number = "529",

}

RIS

TY - JOUR

T1 - A hierarchical model of non-homogeneous Poisson processes for Twitter retweets

AU - Lee, Clement

AU - Wilkinson, Darren J.

N1 - 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

PY - 2019/4/30

Y1 - 2019/4/30

N2 - 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

AB - 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

KW - Bayesian methods

KW - Markov chain Monte Carlo

KW - Model selection

KW - Stochastic processes

U2 - 10.1080/01621459.2019.1585358

DO - 10.1080/01621459.2019.1585358

M3 - Journal article

VL - 115

SP - 1

EP - 15

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

SN - 0162-1459

IS - 529

ER -