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

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.

Eliciting and focusing geographic volunteer work

“…We suspect that familiarity giving no benefit for node repairs means that subjects essentially did not apply personal knowledge to this task; instead, they turned on aerial photos and looked to see whether a node was present. In contrast, in previous work that found familiarity to be the basis of effective work elicitation techniques [5,6], the work elicited did require personal knowledge. It would be interesting to examine tasks in other domains that do not require personal knowledge.…”

“…In one, the researchers wanted users of the MovieLens movie recommendation site to edit information about movies [5]. In the other, they wanted Wikipedia editors to edit articles [6].…”

Crafting the initial user experience to achieve community goals

“…Recommender systems like MovieLens always have relied on users to enter ratings, and they have moved even further in the direction of Open Content. For example, Cosley [5,6] experimented with letting MovieLens users enter movies into the database and edit movie information (actors, director, etc. ).…”

Evaluating compliance-without-pressure techniques for increasing participation in online communities

“…Social production communities [2,14] let loosely-connected users work together to produce information and artifacts of value [10]. Collaborative filtering systems like MovieLens 2 and Amazon leverage users' ratings of items (movies, consumer products, etc.)…”