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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Fonseca, A. & Louçã, Jorge (2018). Explaining the emergence of online popularity through a model of information diffusion. Computational and Mathematical Organization Theory. 24 (2), 169-187
Exportar Referência (IEEE) 
A. J. Fonseca and J. M. Louçã,  "Explaining the emergence of online popularity through a model of information diffusion", in Computational and Mathematical Organization Theory, vol. 24, no. 2, pp. 169-187, 2018
Exportar BibTeX 
@article{fonseca2018_1775728874852,
	author = "Fonseca, A. and Louçã, Jorge",
	title = "Explaining the emergence of online popularity through a model of information diffusion",
	journal = "Computational and Mathematical Organization Theory",
	year = "2018",
	volume = "24",
	number = "2",
	doi = "10.1007/s10588-017-9253-5",
	pages = "169-187",
	url = "http://link.springer.com/article/10.1007%2Fs10588-017-9253-5"
}
Exportar RIS 
TY  - JOUR
TI  - Explaining the emergence of online popularity through a model of information diffusion
T2  - Computational and Mathematical Organization Theory
VL  - 24
IS  - 2
AU  - Fonseca, A.
AU  - Louçã, Jorge
PY  - 2018
SP  - 169-187
SN  - 1381-298X
DO  - 10.1007/s10588-017-9253-5
UR  - http://link.springer.com/article/10.1007%2Fs10588-017-9253-5
AB  - This paper proposes a new formal modeling approach to popularity dynamics based on a generic notion of message propagation within society. The approach is demonstrated with two original models of information diffusion. These are a branching model of popularity and a epidemic model of popularity. The first is based on the principles of a branching process, while the second emulates an epidemic equation with a specific infection rate. This allows us to consider the replication phenomena on information diffusion. The approach is validated using a very large dataset collected online that involves keywords in blogs and hashtags on Twitter. Our main results point to an overall good fit of both models, both when the process of popularity grows and when it decays. This is due to endogenous information transfer, as in an epidemic process, but also when the process is initially triggered by an external event. Overall, on balance, our models confirm that popularity builds through message diffusion, which is of the multiplicative kind.
ER  -