A Hierarchical Model of Nonhomogeneous Poisson Processes for Twitter Retweets

• 成果类型: Article
• 署名作者: Lee, Clement; Wilkinson, Darren J.
• 署名单位: Newcastle University - UK; Newcastle University - UK; Alan Turing Institute
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1585358
• 发表日期: 2020
• 页码: 1-15
• 关键词: Bayesian model; DYNAMICS

摘要

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. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement