Please use this identifier to cite or link to this item: http://hdl.handle.net/10071/16797
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dc.contributor.authorRosa, H.-
dc.contributor.authorCarvalho, J. P.-
dc.contributor.authorAstudillo, R.-
dc.contributor.authorBatista, F.-
dc.contributor.editorKóczy, László T.; Medina, Jesús-
dc.date.accessioned2018-11-29T16:40:11Z-
dc.date.available2018-11-29T16:40:11Z-
dc.date.issued2018-
dc.identifier.isbn9783319746807-
dc.identifier.issn1860-949X-
dc.identifier.urihttps://ciencia.iscte-iul.pt/id/ci-pub-50915-
dc.identifier.urihttp://hdl.handle.net/10071/16797-
dc.description.abstractMicroblogs, 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.eng
dc.language.isoeng-
dc.publisherSpringer-
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/132048/PT-
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147282/PT-
dc.rightsopenAccess-
dc.subjectPage rankeng
dc.subjectKatzeng
dc.subjectUser influenceeng
dc.subjectTwittereng
dc.subjectData miningeng
dc.titlePage rank versus katz: is the centrality algorithm choice relevant to measure user influence in Twitter?eng
dc.typebookPart-
dc.event.locationChameng
dc.event.date2018-
dc.peerreviewedyes-
dc.journalInteractions Between Computational Intelligence and Mathematics. Studies in Computational Intelligence-
dc.volume758-
degois.publication.locationChameng
degois.publication.titlePage rank versus katz: is the centrality algorithm choice relevant to measure user influence in Twitter?eng
dc.description.versioninfo:eu-repo/semantics/acceptedVersion-
dc.identifier.doi10.1007/978-3-319-74681-4_1-
dc.subject.fosDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informaçãopor
dc.date.embargo2019-05-29
Appears in Collections:CTI-CLI - Capítulos de livros internacionais

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