Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2024

Abstract

We propose the Burst-Induced Poisson Process (BPoP), a model designed to analyze time series data such as feeds or search queries. BPoP can distinguish between the slowly-varying regular activity of a stable audience and the bursty activity of a curious audience, often seen in viral threads. Our model consists of two hidden, interacting processes: a self-feeding process (SFP) that generates bursty behavior related to viral threads, and a non-homogeneous Poisson process (NHPP) with step function intensity that is influenced by the bursts from the SFP. The NHPP models the normal background behavior, driven solely by the overall popularity of the topic among the stable audience. Through extensive empirical work, we have demonstrated that our model fits and characterizes a large number of real datasets more effectively than state-of-the-art models. Most importantly, BPoP can quantify the stable audience of media channels over time, serving as a valuable indicator of their popularity.

Keywords

Time Series, Point Processes, EM algorithm

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

WWW '24: Proceedings of the ACM Web Conference 2024, Singapore, May 13-17

First Page

2464

Last Page

2475

ISBN

9798400701719

Identifier

10.1145/3589334.36454

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3589334.36454

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