We present a novel model for classifying the quality of Wikipedia articles based on structural properties of a network representation of the article's revision history. We create revision history networks (an adaptation of Keegan et. al's article trajectory networks [7]), where nodes correspond to individual editors of an article, and edges join the authors of consecutive revisions. Using descriptive statistics generated from these networks, along with general properties like the number of edits and article size, we predict which of six quality classes (Start, Stub, C-Class, B-Class, Good, Featured) articles belong to, attaining a classification accuracy of 49.35% on a stratified sample of articles. These results suggest that structures of collaboration underlying the creation of articles, and not just the content of the article, should be considered for accurate quality classification.

Prior research suggests that facilitating easier communication in social computing systems will increase both interpersonal interactions as well as group productivity. This study tests these claims by examining the impact of a new communication feature called "message walls" that allows for faster and more intuitive interpersonal communication in wikis. Using panel data from a sample of 275 wiki communities that migrated to message walls and a method inspired by regression discontinuity designs, we analyze these transitions and estimate the impact of the system's introduction. Although the adoption of message walls was associated with increased communication among all editors and newcomers, it had little effect on productivity, and was further associated with a decrease in article contributions from new editors. Our results imply that design changes that make communication easier in a social computing system may not translate to increased participation along other dimensions.

Integrating new users into a community with complex norms presents a challenge for peer production projects like Wikipedia. We present The Wikipedia Adventure (TWA): an interactive tutorial that offers a structured and gamified introduction to Wikipedia. In addition to describing the design of the system, we present two empirical evaluations. First, we report on a survey of users, who responded very positively to the tutorial. Second, we report results from a large-scale invitation-based field experiment that tests whether using TWA increased newcomers' subsequent contributions to Wikipedia. We find no effect of either using the tutorial or of being invited to do so over a period of 180 days. We conclude that TWA produces a positive socialization experience for those who choose to use it, but that it does not alter patterns of newcomer activity. We reflect on the implications of these mixed results for the evaluation of similar social computing systems.