A Hierarchical Model of Nonhomogeneous Poisson Processes for Twitter Retweets
提出了一个分层非齐次泊松过程模型，用于建模推特转发行为，通过关注者数量等协变量预测转发次数，并利用贝叶斯因子进行模型选择。
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