Examinando por Autor "Cuevas R."

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    Author Correction: Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks (Scientific Reports, (2018), 8, 1, (6955), 10.1038/s41598-018-24874-2)
    (Nature Publishing Group, 2019-01-01) Azcorra A.; Chiroque L.F.; Cuevas R.; Anta A.F.; Laniado H.; Lillo R.E.; Romo J.; Sguera C.; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    The original version of this Article contained an error in Affiliation 3, which was incorrectly given as ‘Department of Mathematical Sciences, Universidad EAFIT, Universidad Nacional de Colombia, Medellín, Colombia’. The correct affiliation is listed below: Department of Mathematical Sciences, Universidad EAFIT, Medellín, Colombia This error has now been corrected in the HTML and PDF versions of the Article and in the accompanying Supplementary Material file. © 2019, The Author(s).
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    Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks
    (Nature Publishing Group, 2018-05-03) Azcorra A.; Chiroque L.F.; Cuevas R.; Fernández Anta A.; Laniado H.; Lillo R.E.; Romo J.; Sguera C.; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    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. © 2018 The Author(s).