title: Searching for superspreaders of information in real-world social media.
abstract: A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.
authors.affiliation: 1] LMIB and School of Mathematics and Systems Science, Beihang University, Beijing, 100191, China [2] Levich Institute and Physics Department, City College of New York, New York, NY 10031, USA.
authors.forename: Sen
authors.initials: S
authors.lastname: Pei
authors.affiliation: School of Business Administration, The Hebrew University of Jerusalem, 91905 Israel.
authors.forename: Lev
authors.initials: L
authors.lastname: Muchnik
authors.affiliation: Departamento de Física, Universidade Federal do Ceará, 60451-970, Fortaleza, Ceará, Brazil.
authors.forename: José S
authors.initials: JS
authors.lastname: Andrade
authors.affiliation: LMIB and School of Mathematics and Systems Science, Beihang University, Beijing, 100191, China.
authors.forename: Zhiming
authors.initials: Z
authors.lastname: Zheng
authors.affiliation: 1] Levich Institute and Physics Department, City College of New York, New York, NY 10031, USA [2] Departamento de Física, Universidade Federal do Ceará, 60451-970, Fortaleza, Ceará, Brazil.
authors.forename: Hernán A
authors.initials: HA
authors.lastname: Makse
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D033181:Information Dissemination; D007857:Leadership; D008962:Models, Theoretical; D061108:Social Media; D060756:Social Networking