Twitter转发的非齐次Poisson过程分层模型
A hierarchical model of non-homogeneous Poisson processes for Twitter retweets
摘要：
我们提出了一个层次模型的非齐次泊松过程（NHPP）的信息扩散在网上的社交媒体，特别是Twitter的转发。每个原始推文的转发由NHPP建模，其强度函数是时间衰减分量和取决于原始推文作者的追随者计数的另一个分量的乘积。后者允许我们通过网络中心度相关的协变量来解释或预测最终的转发计数。推理算法能够计算贝叶斯因子，以方便模型选择。最后，将该模型应用于两个主题标签的转推数据集。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
