Exportar Publicação

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). How things become popular: a probabilistic approach for online popularity. Social Science Computer Review. 36 (2), 176-194
Exportar Referência (IEEE) 
A. J. Fonseca and J. M. Louçã,  "How things become popular: a probabilistic approach for online popularity", in Social Science Computer Review, vol. 36, no. 2, pp. 176-194, 2018
Exportar BibTeX 
@article{fonseca2018_1775764792721,
	author = "Fonseca, A. and Louçã, Jorge",
	title = "How things become popular: a probabilistic approach for online popularity",
	journal = "Social Science Computer Review",
	year = "2018",
	volume = "36",
	number = "2",
	doi = "10.1177/0894439317707175",
	pages = "176-194",
	url = "http://journals.sagepub.com"
}
Exportar RIS 
TY  - JOUR
TI  - How things become popular: a probabilistic approach for online popularity
T2  - Social Science Computer Review
VL  - 36
IS  - 2
AU  - Fonseca, A.
AU  - Louçã, Jorge
PY  - 2018
SP  - 176-194
SN  - 0894-4393
DO  - 10.1177/0894439317707175
UR  - http://journals.sagepub.com
AB  - This work discusses the mechanisms of popularity generation on the Internet. What we propose here is a model that replicates the statistical distribution profile of popularity. It is a probabilistic model of the number of individuals who read, hear or see, and then replicate a message, and parameterizes an individual’s preference for either new or older messages. Messages can gain in popularity according to a process of paying attention and the resulting popularity distribution has a stretched lognormal configuration. The stretch depends on the degree of attention paid to new messages versus that paid to older messages. We considered three sets of data to test the fit of the model: the American singers/songwriters listed on Wikipedia, videos on YouTube belonging to two different categories, and the number of visits to Wikipedia pages on music albums and film categories. Our main results adjust, with good approximation, to this experimental data. In each of the three case studies, the fit produced by the model is better adjusted to the data than the lognormal standard function.
ER  -