Crowdsourcing, Attention and Productivity Export

@unpublished{citeulike:3294999, abstract = {The tragedy of the digital commons does not prevent the copious voluntary production of content that one witnesses in the web. 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, uploaders compare themselves to others when having low productivity and to themselves when exceeding a threshold.}, author = {Huberman, Bernardo A. and Romero, Daniel M. and Wu, Fang}, citeulike-article-id = {3294999}, citeulike-linkout-0 = {http://www.hpl.hp.com/research/scl/papers/crowd/crowd.pdf}, keywords = {attention\_economy, online\_communities, social\_software, web\_epistemology}, posted-at = {2008-09-25 15:33:13}, priority = {3}, title = {Crowdsourcing, Attention and Productivity}, url = {http://www.hpl.hp.com/research/scl/papers/crowd/crowd.pdf} }

TY - INPR ID - citeulike:3294999 L3 - citeulike-article-id:3294999 N2 - The tragedy of the digital commons does not prevent the copious voluntary production of content that one witnesses in the web. 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, uploaders compare themselves to others when having low productivity and to themselves when exceeding a threshold. TI - Crowdsourcing, Attention and Productivity KW - attention_economy KW - online_communities KW - social_software KW - web_epistemology AU - Huberman, Bernardo AU - Romero, Daniel AU - Wu, Fang UR - http://www.hpl.hp.com/research/scl/papers/crowd/crowd.pdf ER -