Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks

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dc.citation.journalTitleSCIENTIFIC REPORTSeng
dc.contributor.authorAzcorra A.
dc.contributor.authorChiroque L.F.
dc.contributor.authorCuevas R.
dc.contributor.authorFernández Anta A.
dc.contributor.authorLaniado H.
dc.contributor.authorLillo R.E.
dc.contributor.authorRomo J.
dc.contributor.authorSguera C.
dc.contributor.departmentUniversidad EAFIT. Escuela de Cienciasspa
dc.contributor.researchgroupModelado Matemáticospa
dc.date.accessioned2021-04-12T14:07:15Z
dc.date.available2021-04-12T14:07:15Z
dc.date.issued2018-05-03
dc.description.abstractBillions of users interact intensively every day via Online Social Networks (OSNs) such as Facebook, Twitter, or Google+. This makes OSNs an invaluable source of information, and channel of actuation, for sectors like advertising, marketing, or politics. To get the most of OSNs, analysts need to identify influential users that can be leveraged for promoting products, distributing messages, or improving the image of companies. In this report we propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), based on outliers detection, for providing support in the identification of influential users. MUOD is scalable, and can hence be used in large OSNs. Moreover, it labels the outliers as of shape, magnitude, or amplitude, depending of their features. This allows classifying the outlier users in multiple different classes, which are likely to include different types of influential users. Applying MUOD to a subset of roughly 400 million Google+ users, it has allowed identifying and discriminating automatically sets of outlier users, which present features associated to different definitions of influential users, like capacity to attract engagement, capacity to attract a large number of followers, or high infection capacity. © 2018 The Author(s).eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8073
dc.identifier.doi10.1038/s41598-018-24874-2
dc.identifier.issn20452322
dc.identifier.otherWOS;000431291500007
dc.identifier.otherPUBMED;29725046
dc.identifier.otherSCOPUS;2-s2.0-85046544439
dc.identifier.urihttp://hdl.handle.net/10784/27796
dc.language.isoengeng
dc.publisherNature Publishing Group
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85046544439&doi=10.1038%2fs41598-018-24874-2&partnerID=40&md5=08373b6e000312f354c7fa94bf4685b5
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/2045-2322
dc.sourceSCIENTIFIC REPORTS
dc.subject.keywordOUTLIER DETECTIONeng
dc.subject.keywordCOMPLEX NETWORKSeng
dc.subject.keywordFUNCTIONAL DATAeng
dc.subject.keywordDEPTHeng
dc.subject.keywordFMRIeng
dc.titleUnsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networkseng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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