%0 Journal Article %1 WOS:000338421300002 %A Pei, Sen %A Muchnik, Lev %A Jr., Jose S Andrade %A Zheng, Zhiming %A Makse, Hernan A %C MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND %D 2014 %I NATURE PUBLISHING GROUP %J SCIENTIFIC REPORTS %K imported %R 10.1038/srep05547 %T Searching for superspreaders of information in real-world social media %V 4 %X 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.

@article{WOS:000338421300002, 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.}, added-at = {2022-05-23T20:00:14.000+0200}, address = {MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND}, author = {Pei, Sen and Muchnik, Lev and Jr., Jose S Andrade and Zheng, Zhiming and Makse, Hernan A}, biburl = {https://www.bibsonomy.org/bibtex/2e563fe21c3aa7871a983f1a647e86316/ppgfis_ufc_br}, doi = {10.1038/srep05547}, interhash = {9155a9d5da3a6538f6dafa7a27479b14}, intrahash = {e563fe21c3aa7871a983f1a647e86316}, issn = {2045-2322}, journal = {SCIENTIFIC REPORTS}, keywords = {imported}, publisher = {NATURE PUBLISHING GROUP}, pubstate = {published}, timestamp = {2022-05-23T20:00:14.000+0200}, title = {Searching for superspreaders of information in real-world social media}, tppubtype = {article}, volume = 4, year = 2014 }