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Publication

Effects of network structure, competition and memory time on social spreading phenomena

Date
2016
Abstract
Online social media has greatly affected the way in which we communicate with each other. However, little is known about what fundamental mechanisms drive dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior that is analytically tractable and that can reproduce several characteristics of empirical micro-blogging data on hashtag usage, such as (time-dependent) heavy-tailed distributions of meme popularity. The presented framework constitutes a null model for social spreading phenomena that, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.
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Description
peer-reviewed
Publisher
American Physical Society
Citation
Physical Review X;6, 021019
Collections
Mathematics & Statistics
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Gleeson_2016_effects.pdf
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Keywords
hidden influentials, random drift, heavy tails, online
ULRR Identifiers
https://researchrepository.ul.ie/handle/10344/5157
 https://doi.org/10.34961/4210
Funding code
Funding Information
Sustainable Development Goals
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Type
Article
Rights
https://creativecommons.org/licenses/by-nc-sa/1.0/
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