Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/43655
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dc.contributor.authorAmati, Giambattistait
dc.contributor.authorAngelini, Simoneit
dc.contributor.authorGambosi, Giorgioit
dc.contributor.authorRossi, Gianlucait
dc.contributor.authorVocca, Paolait
dc.date.accessioned2021-10-13T17:17:08Z-
dc.date.available2021-10-13T17:17:08Z-
dc.date.issued2019it
dc.identifier.issn1380-7501it
dc.identifier.urihttp://hdl.handle.net/2067/43655-
dc.description.abstractIn this paper, we study how to detect the most influential users in the microblogging social network platform Twitter and their evolution over time. To this aim, we consider the Dynamic Retweet Graph (DRG) proposed in Amati et al. (2016) and partially analyzed in Amati et al. (IADIS Int J Comput Sci Inform Syst, 11(2) 2016), Amati et al. (2016). The model of the evolution of the Twitter social network is based here on the retweet relationship. In a DRGs, the last time a tweet has been retweeted we delete all the edges representing this tweet. In this way we model the decay of tweet life in the social platform. To detect the influential users, we consider the central nodes in the network with respect to the following centrality measures: degree, closeness, betweenness and PageRank-centrality. These measures have been widely studied in the static case and we analyze them on the sequence of DRG temporal graphs with special regard to the distribution of the 75 % most central nodes. We derive the following results: (a) in all cases, applying the closeness measure results into many nodes with high centrality, so it is useless to detect influential users; (b) for all other measures, almost all nodes have null or very low centrality and (c) the number of vertices with significant centrality are often the same; (d) the above observations hold also for the cumulative retweet graph and, (e) central nodes in the sequence of DRG temporal graphs have high centrality in cumulative graph.it
dc.language.isoengit
dc.titleInfluential users in Twitter: detection and evolution analysisit
dc.typearticle*
dc.identifier.doi10.1007/s11042-018-6728-4it
dc.identifier.scopus2-s2.0-85056884211it
dc.identifier.isiWOS:000458171600040it
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85056884211it
dc.relation.journalMULTIMEDIA TOOLS AND APPLICATIONSit
dc.relation.firstpage3395it
dc.relation.lastpage3407it
dc.relation.volume78it
dc.relation.issue3it
dc.subject.scientificsectorINF/01it
dc.subject.keywordsGraph analysisit
dc.subject.keywordsSocial mediait
dc.subject.keywordsTwitterit
dc.description.numberofauthors5it
dc.description.internationalnoit
dc.contributor.countryITAit
dc.type.refereeREF_1it
dc.type.miur262*
item.languageiso639-1en-
item.grantfulltextrestricted-
item.openairetypearticle-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.journal.journalissn1380-7501-
crisitem.journal.anceE113944-
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