Using intelligent task routing and contribution review to help communities build artifacts of lasting value

  • Authors:

  • Dan Cosley;Dan Frankowski;Loren Terveen;John Riedl

  • Affiliations:

  • University of Minnesota, Minnesota, MN;University of Minnesota, Minnesota, MN;University of Minnesota, Minnesota, MN;University of Minnesota, Minnesota, MN

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2006

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Abstract

Many online communities are emerging that, like Wikipedia, bring people together to build community-maintained artifacts of lasting value (CALVs). Motivating people to contribute is a key problem because the quantity and quality of contributions ultimately determine a CALV's value. We pose two related research questions: 1) How does intelligent task routing---matching people with work---affect the quantity of contributions? 2) How does reviewing contributions before accepting them affect the quality of contributions? A field experiment with 197 contributors shows that simple, intelligent task routing algorithms have large effects. We also model the effect of reviewing contributions on the value of CALVs. The model predicts, and experimental data shows, that value grows more slowly with review before acceptance. It also predicts, surprisingly, that a CALV will reach the same final value whether contributions are reviewed before or after they are made available to the community.