Unsupervised scalable statistical method for identifying influential users in online social networks

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dc.contributor.author Azcorra Saloña, Arturo
dc.contributor.author Chiroque, Luis
dc.contributor.author Cuevas Rumín, Rubén
dc.contributor.author Fernández Anta, A.
dc.contributor.author Laniado Rodas, Henry
dc.contributor.author Lillo Rodríguez, Rosa Elvira
dc.contributor.author Romo Urroz, Juan
dc.contributor.author Sguera, Carlo
dc.date.accessioned 2018-08-21T10:42:13Z
dc.date.available 2018-08-21T10:42:13Z
dc.date.issued 2018-05-03
dc.identifier.bibliographicCitation Scientific Reports, (2018), 8: 6955.
dc.identifier.issn 2045-2322
dc.identifier.uri http://hdl.handle.net/10016/27307
dc.description.abstract Billions 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.
dc.description.sponsorship Partially supported by Ministerio de Economia y Competitividad grant ECO2015-66593-P, Regional Government of Madrid (CM) grant Cloud4BigData (S2013/ICE-2894, co-funded by FSE & FEDER), the European Union through the ReCRED (653417) project and MIT MISTI Global Seed Funds through the MyBubble project.
dc.format.extent 7
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.rights © The Author(s) 2018
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Online social networks
dc.subject.other Massive Unsupervised Outlier Detection (MUOD)
dc.subject.other Detection algorithms
dc.title Unsupervised scalable statistical method for identifying influential users in online social networks
dc.type article
dc.relation.publisherversion https://www.nature.com/articles/s41598-018-24874-2
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1038/s41598-018-24874-2
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ECO2015-66593-P
dc.relation.projectID Comunidad de Madrid. S2013/ICE-2894
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/653417
dc.type.version publishedVersion
dc.identifier.publicationtitle Scientific Reports
dc.identifier.publicationvolume 8
dc.identifier.uxxi AR/0000021449
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