Crowdsourcing, attention and productivity

Authors:
Bernardo A. Huberman;Daniel M. Romero;Fang Wu
Affiliations:
Social Computing Lab, HP Laboratories, Palo Alto, CA,USA;Center for Applied Mathematics, Cornell University,Ithaca, NY, USA;Social Computing Lab, HP Laboratories, Palo Alto, CA,USA
Venue:
Journal of Information Science
Year:
2009

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Abstract

We show through an analysis of a massive data set from YouTube that the productivity exhibited in crowdsourcing exhibits a strong positive dependence on attention, measured by the number of downloads. Conversely, a lack of attention leads to a decrease in the number of videos uploaded and the consequent drop in productivity, which in many cases asymptotes to no uploads whatsoever. Moreover, short-term contributors compare their performance to the average contributor芒聙聶s performance while long-term contributors compare it to their own media.