Unraveling the Dynamics of Stable and Curious Audiences in Web Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F24%3A00381148" target="_blank" >RIV/68407700:21240/24:00381148 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3589334.3645473" target="_blank" >http://dx.doi.org/10.1145/3589334.3645473</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3589334.3645473" target="_blank" >10.1145/3589334.3645473</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unraveling the Dynamics of Stable and Curious Audiences in Web Systems

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Unraveling the Dynamics of Stable and Curious Audiences in Web Systems

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the ACM Web Conference 2024

ISBN

979-8-4007-0171-9

ISSN

—

e-ISSN

—

Počet stran výsledku

12

Strana od-do

2464-2475

Název nakladatele

ACM

Místo vydání

New York

Místo konání akce

Singapore

Datum konání akce

13. 5. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—