Who adds the most value to Wikipedia? Which types of edits are most valuable? How do we call attention to high-value opportunities to contribute? While some research projects have touched on related issues (e.g.  and ), there have been no thorough, systematic studies that have broken down value-adding behaviors per edit to explore overall trends. In this collection of projects, we'll explore the fundamental nature of encyclopedic value as well as practical measurement strategies.
Formalization of valueEdit
In this project, we will adopt the above formulation. value is the product of the quality and importance of content. In the case of Wikipedia, encyclopedia articles are the primary artifact of importance. This formalization assumes that the value of high quality articles is weighted by their importance. In other words, it is particularly desirable to have important articles be of high quality.
In order to increase value, productive contributions are made to articles. The more important the article, the more value productive edits add to Wikipedia.
Value is a big and complex concept. In order to measure value effectively and robustly, we'll need to explore different aspects of value measurement. These themes can be explored independently in sub-projects.
- How do human-assessed importance measures (e.g. WikiProject importance classes) compare to consumption patterns (e.g. view rate) and wiki structure (e.g. # of inlinks)?
- Which readers (in geographic regions/ language, by their engagement -how much they read?) read this article?
- What signals exist for measuring the quality of an edit?
- How do human-assessments of productivity compare with edit productivity measurements?
- Do different types of users (editors, readers, Westerners, Africans, etc.) disagree on the productivity of an edit?
Experimenting with value presentationEdit
- See WikiCredit
- How does the presentation of per-user value-added measurements affect editor behavior?
- How will editors try to game value-added measurements and what countermeasures will be effective?
- Who adds the most value to Wikipedia? How effectively direct editors toward high-value opportunities to contribute?
- Database dumps
- Wikimedia Labs
- WikiBrainAPI -- A java library for importing and doing intelligent API work with MediaWiki data (Java)
- MediaWiki Utilities -- Querying and processing MediaWiki datasource (Python)
- Deltas -- State of the art difference algorithms (Python)
- Wiki-Class -- Automatic article quality classification (Python)
- Priedhorsky, R., Chen, J., Lam, S. T. K., Panciera, K., Terveen, L., & Riedl, J. (2007, November). Creating, destroying, and restoring value in Wikipedia. In Proceedings of the 2007 international ACM conference on Supporting group work (pp. 259-268). ACM.