Searching for superspreaders of information in real-world social media

Sen Pei, Lev Muchnik, José S. Andrade, Zhiming Zheng, Hernán A. Makse*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

314 Scopus citations

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 viralâinformation dissemination in relevant applications.

Original languageAmerican English
Article number5547
JournalScientific Reports
Volume4
DOIs
StatePublished - 3 Jul 2014

Bibliographical note

Funding Information:
This work was supported by NSF, NIH and ARL under Cooperative Agreement Number W911NF-09-2-0053. S.P. was supported by NSFC (No. 11290141, 11201018), 2010DFR00700, MJ-F-2012-04 and Innovation Foundation of BUAA for PhD Graduates. J.S.A. would like to thank the Brazilian agencies CNPq, CAPES and FUNCAP for financial support. We thank L.K. Gallos and Y. Hu for useful discussions and M. Doyle and H.D. Rozenfeld for providing the APS dataset.

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