The next Research & Data showcase will be live-streamed this Wednesday, 8/20 at 11.30 PT.
The streaming link will be posted on the lists a few minutes before the showcase starts and as usual, you can join the conversation on IRC at #wikimedia-research.
We look forward to seeing you!
Everything You Know About Mobile Is WrW^Right: Editing and Reading Pattern Variation Between User Types
By Oliver Keyes: Using new geolocation tools, we look at reader and editor behaviour to understand how and when people access and contribute to our content. This is largely exploratory research, but has potential implications for our A/B testing and how we understand both cultural divides between reader and editor groups from different countries, and how we understand the differences between types of edit and the editors who make them.
Wikipedia article curation: understanding quality, recommending tasks
By Morten Warncke-Wang*: In this talk we look at article curation in Wikipedia through the lens of task suggestions and article quality. The first part of the talk presents SuggestBot, the Wikipedia article recommender. SuggestBot connects contributors with articles similar to those they previously edited. In the second part of the talk, we discuss Wikipedia article quality using “actionable” features, features that contributors can easily act upon to improve article quality. We will first discuss these features’ ability to predict article quality, before coming back to SuggestBot and show how these predictions and actionable features can be used to improve the suggestions.
*Bio: Morten Warncke-Wang is a PhD student at the GroupLens research lab, University of Minnesota. His main research focus is artefact quality and task recommendations in peer production communities. On the task recommendation side he has maintained the Wikipedia article recommender SuggestBot (http://en.wikipedia.org/wiki/User:SuggestBot) since 2010, expanding it to support six languages and additional information about recommended articles. His work on artefact quality looks at understanding quality through features contributors can easily improve, using them to both predict Wikipedia article quality and suggest improvement tasks to Wikipedia contributors.
You can find more information about his research on his homepage: