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

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.

[1]  Audris Mockus,et al.  Expertise Browser: a quantitative approach to identifying expertise , 2002, Proceedings of the 24th International Conference on Software Engineering. ICSE 2002.

[2]  Gregory B. Newby,et al.  Distributed proofreading , 2003, 2003 Joint Conference on Digital Libraries, 2003. Proceedings..

[3]  M. V Mederos,et al.  Gautschi, Walter. Numerical analysis: an introduction, Birkhäuser, 1997 , 1999 .

[4]  Paul Resnick,et al.  Slash(dot) and burn: distributed moderation in a large online conversation space , 2004, CHI.

[5]  Mark S. Ackerman,et al.  Expertise recommender: a flexible recommendation system and architecture , 2000, CSCW '00.

[6]  Robert E. Kraut,et al.  Experiment 1 : Motivating Conversational Contributions Through Group Homogeneity and Individual Uniqueness , 2010 .

[7]  John Riedl,et al.  How oversight improves member-maintained communities , 2005, CHI.

[8]  Martin Wattenberg,et al.  Studying cooperation and conflict between authors with history flow visualizations , 2004, CHI.

[9]  Dan Cosley,et al.  Think different: increasing online community participation using uniqueness and group dissimilarity , 2004, CHI.

[10]  Kipling D. Williams,et al.  Interpersonal Relations and Group Processes Social Loafing: a Meta-analytic Review and Theoretical Integration , 2022 .

[11]  James D. Hollan,et al.  Edit wear and read wear , 1992, CHI.

[12]  Laura A. Dabbish,et al.  Labeling images with a computer game , 2004, AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors.

[13]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

[14]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.