Atlantis Institut des Sciences Fictives

Resumen: 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.
Idioma: Inglés
DOI: 10.1103/PhysRevX.6.021019
Año: 2016
Publicado en: Physical review. X 6, 2 (2016), 021019 [22 pp]
ISSN: 2160-3308
Factor impacto JCR: 12.789 (2016)
Categ. JCR: PHYSICS, MULTIDISCIPLINARY rank: 5 / 79 = 0.063 (2016) - Q1 - T1
Factor impacto SCIMAGO: 7.434 - Physics and Astronomy (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/FENOL-GROUP
Financiación: info:eu-repo/grantAgreement/EC/FP7/317532/EU/Foundational Research on MULTIlevel comPLEX networks and systems/MULTIPLEX
Financiación: info:eu-repo/grantAgreement/EC/FP7/317614/EU/Mathematical framework for multiplex networks/PLEXMATH
Financiación: info:eu-repo/grantAgreement/ES/MINECO/FIS2011-25167
Tipo y forma: Article (Published version)
Área (Departamento): Área Física Teórica (Dpto. Física Teórica)
Exportado de SIDERAL (2020-09-10-14:38:40)


Visitas y descargas

Este artículo se encuentra en las siguientes colecciones:
articulos