Scalable Content Curation: Learning From Human Effort

Creative agencies often crowdsource to generate ideas for new content or products. We find that some evaluators benefit disproportionately from the application of a statistical model, a phenomenon we term “predictable inaccuracy.” Our framework yields metrics for evaluator importance and assesses the extent to which a model substitutes human effort.



Citation:

Pavel Kireyev, Artem Timoshenko, and Cathy Liu Yang (2020) ,"Scalable Content Curation: Learning From Human Effort", in NA - Advances in Consumer Research Volume 48, eds. Jennifer Argo, Tina M. Lowrey, and Hope Jensen Schau, Duluth, MN : Association for Consumer Research, Pages: 831-835.

Authors

Pavel Kireyev, INSEAD, France
Artem Timoshenko, Northwestern University, USA
Cathy Liu Yang, HEC Paris, France



Volume

NA - Advances in Consumer Research Volume 48 | 2020



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