Using intelligent task rooting and contribution review to help communities build artifacts of lasting value
|Using intelligent task rooting and contribution review to help communities build artifacts of lasting value|
|Author(s)||Cosley D., Frankowski D., Terveen L., Riedl J.|
|Published in||Conference on Human Factors in Computing Systems - Proceedings|
|Keyword(s)||Contribution models, Editorial review, Intelligent task routing, Member-maintained, Online communities, Wikipedia (Extra: Contribution models, Editorial review, Intelligent task routing, Member-maintained, Online communities, Wikipedia, Algorithms, Behavioral research, Data acquisition, Human computer interaction, Intelligent agents, Predictive control systems, Virtual reality, Electronic communities)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
Using intelligent task rooting and contribution review to help communities build artifacts of lasting value is a 2006 conference paper written in English by Cosley D., Frankowski D., Terveen L., Riedl J. and published in Conference on Human Factors in Computing Systems - Proceedings.
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 CALVs 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. Copyright 2006 ACM.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers. Cited 9 time(s)