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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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -