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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)
Rosa, H., Carvalho, J. P., Astudillo, R. & Batista, F. (2018). Page rank versus katz: is the centrality algorithm choice relevant to measure user influence in Twitter?. In Kóczy, László T.; Medina, Jesús (Ed.), Interactions Between Computational Intelligence and Mathematics. Studies in Computational Intelligence. Cham: Springer.
Exportar Referência (IEEE)
H. Rosa et al.,  "Page rank versus katz: is the centrality algorithm choice relevant to measure user influence in Twitter?", in Interactions Between Computational Intelligence and Mathematics. Studies in Computational Intelligence, Kóczy, László T.; Medina, Jesús, Ed., Cham, Springer, 2018, vol. 758
Exportar BibTeX
@incollection{rosa2018_1714823108565,
	author = "Rosa, H. and Carvalho, J. P. and Astudillo, R. and Batista, F.",
	title = "Page rank versus katz: is the centrality algorithm choice relevant to measure user influence in Twitter?",
	chapter = "",
	booktitle = "Interactions Between Computational Intelligence and Mathematics. Studies in Computational Intelligence",
	year = "2018",
	volume = "758",
	series = "",
	edition = "",
	publisher = "Springer",
	address = "Cham",
	url = "https://link.springer.com/chapter/10.1007%2F978-3-319-74681-4_1"
}
Exportar RIS
TY  - CHAP
TI  - Page rank versus katz: is the centrality algorithm choice relevant to measure user influence in Twitter?
T2  - Interactions Between Computational Intelligence and Mathematics. Studies in Computational Intelligence
VL  - 758
AU  - Rosa, H.
AU  - Carvalho, J. P.
AU  - Astudillo, R.
AU  - Batista, F.
PY  - 2018
SN  - 1860-949X
DO  - 10.1007/978-3-319-74681-4_1
CY  - Cham
UR  - https://link.springer.com/chapter/10.1007%2F978-3-319-74681-4_1
AB  - Microblogs, such as Twitter, have become an important socio-political analysis tool. One of the most important tasks in such analysis is the detection of relevant actors within a given topic through data mining, i.e., identifying who are the most influential participants discussing the topic. Even if there is no gold standard for such task, the adequacy of graph based centrality tools such as PageRank and Katz is well documented. In this paper, we present a case study based on a "London Riots'' Twitter database, where we show that Katz is not as adequate for the task of important actors detection since it fails to detect what we refer to as "indirect gloating'', the situation where an actor capitalizes on other actors referring to him.
ER  -