Searching for superspreaders of information in real-world social media

By Sen Pei, Lev Muchnik, José S. Andrade Jr., Zhiming Zheng, Hernán A. Makse. Scientific Reports, 2014

392 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8fb736d1800f3aff34971ebfacefd83f17c67d17

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In Computer Networks and Communications

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In Statistical and Nonlinear Physics

Citations in the corpus

Measuring user influence on Twitter: A survey (2015, methodology): “Although this algorithm has been used in Twitter directly [101], Borge-Holthoefer and Moreno [12] suggest that it does not work to measure inﬂuential spreaders in rumor dynamics.”
Dynamics of information diffusion and its applications on complex networks (2016, background): “In such platforms, e.g., Facebook, users can post text, images, video, hyperlinks, and the alike to their profile page such that the content can be visible to either a group of defined people (private), or to everyone (public).”
The H-index of a network node and its relation to degree and coreness (2016, background): “Recent studies suggest that coreness is a good measure of a node’s inﬂuence 22,24 .”
Vital nodes identification in complex networks (2016, background): “[59] found that the important nodes are consistently located in the k-core across dissimilar social platforms.”
Identifying influential spreaders in complex networks based on gravity formula (2015, background): “on properly, that is to say, natural systems are organized in interconnected networks [40,41,42]; And some real spreading dynamics like the diﬀusion of rumors or opinions, the rise of scientiﬁc ideas [43,44,45] are diﬀerent from the spreading of epidemic, which may challenge the eﬀectiveness of prop”