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10.1371/journal.pone.0200109 http://scihub22266oqcxt.onion/10.1371/journal.pone.0200109 C6047770!6047770!30011290     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2018 ; 13 (7): ä Nephropedia Template TP {{tp|p=30011290|t=2018. Identification of influencers through the wisdom of crowds |pdf=|usr=}}{{30011290}} gab.com Text Identification of influencers through the wisdom of crowds JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=30011290 #FOAvip Identifying individuals who are influential in diffusing information, ideas or products in a population remains a challenging problem. Most extant work can be abstracted by a process in which researchers first decide which features describe an influencer and then identify them as the individuals with the highest values of these features. This makes the identification dependent on the relevance of the selected features and it still remains uncertain if triggering the identified influencers leads to a behavioral change in others. Furthermore, most work was developed for cross-sectional or time-aggregated datasets, where the time-evolution of influence processes cannot be observed. We show that mapping the influencer identification to a wisdom of crowds problem overcomes these limitations. We present a framework in which the individuals in a social group repeatedly evaluate the contribution of other members according to what they perceive as valuable and not according to predefined features. We propose a method to aggregate the behavioral reactions of the members of the social group into a collective judgment that considers the temporal variation of influence processes. Using data from three large news providers, we show that the members of the group surprisingly agree on who are the influential individuals. The aggregation method addresses different sources of heterogeneity encountered in social systems and leads to results that are easily interpretable and comparable within and across systems. The approach we propose is computationally scalable and can be applied to any social systems where behavioral reactions are observable. Twit Text FOAVip Identification of influencers through the wisdom of crowds ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=30011290 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=30011290 #FOAvip English Wikipedia <ref>{{Cite pmid|30011290}}</ref> Identification of influencers through the wisdom of crowds #MMPMID30011290 Tanase R; Tessone CJ; Algesheimer R PLoS One 2018[]; 13 (7): ä PMID30011290show ga Identifying individuals who are influential in diffusing information, ideas or products in a population remains a challenging problem. Most extant work can be abstracted by a process in which researchers first decide which features describe an influencer and then identify them as the individuals with the highest values of these features. This makes the identification dependent on the relevance of the selected features and it still remains uncertain if triggering the identified influencers leads to a behavioral change in others. Furthermore, most work was developed for cross-sectional or time-aggregated datasets, where the time-evolution of influence processes cannot be observed. We show that mapping the influencer identification to a wisdom of crowds problem overcomes these limitations. We present a framework in which the individuals in a social group repeatedly evaluate the contribution of other members according to what they perceive as valuable and not according to predefined features. We propose a method to aggregate the behavioral reactions of the members of the social group into a collective judgment that considers the temporal variation of influence processes. Using data from three large news providers, we show that the members of the group surprisingly agree on who are the influential individuals. The aggregation method addresses different sources of heterogeneity encountered in social systems and leads to results that are easily interpretable and comparable within and across systems. The approach we propose is computationally scalable and can be applied to any social systems where behavioral reactions are observable. äIdentification of influencers through the wisdom of crowds (epmc).pdf DeepDyve Pubget Overpricing