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Title: Page rank versus katz: is the centrality algorithm choice relevant to measure user influence in Twitter?
Authors: Rosa, H.
Carvalho, J. P.
Astudillo, R.
Batista, F.
Editors: Kóczy, László T.; Medina, Jesús
Keywords: Page rank
User influence
Data mining
Issue Date: 2018
Publisher: Springer
Abstract: 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.
Peer reviewed: yes
DOI: 10.1007/978-3-319-74681-4_1
ISBN: 9783319746807
ISSN: 1860-949X
Appears in Collections:CTI-CLI - Autoria de capítulos de livros internacionais

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