Learning Human Activity Patterns Using Clustered Point Processes With Active and Inactive States

Jingfei Zhang*, Biao Cai, Xuening Zhu, Hansheng Wang, Ganggang Xu, Yongtao Guan

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Citation (Scopus)

Abstract

Modeling event patterns is a central task in a wide range of disciplines. In applications such as studying human activity patterns, events often arrive clustered with sporadic and long periods of inactivity. Such heterogeneity in event patterns poses challenges for existing point process models. In this article, we propose a new class of clustered point processes that alternate between active and inactive states. The proposed model is flexible, highly interpretable, and can provide useful insights into event patterns. A composite likelihood approach and a composite EM estimation procedure are developed for efficient and numerically stable parameter estimation. We study both the computational and statistical properties of the estimator including convergence, consistency, and asymptotic normality. The proposed method is applied to Donald Trump’s Twitter data to investigate if and how his behaviors evolved before, during, and after the presidential campaign. Additionally, we analyze large-scale social media data from Sina Weibo and identify interesting groups of users with distinct behaviors.

© 2022 American Statistical Association
Original languageEnglish
Pages (from-to)388-398
JournalJournal of Business and Economic Statistics
Volume41
Issue number2
Online published11 Feb 2022
DOIs
Publication statusPublished - 2023
Externally publishedYes

Research Keywords

  • Clustered point processes
  • Composite likelihood
  • Composite EM algorithm
  • Nonoverlapping clusters
  • Social media

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