Peer production platforms like Wikipedia commonly suffer from content gaps. Prior research suggests recommender systems can help solve this problem, by guiding editors towards underrepresented topics. However, it remains unclear whether this approach would result in less relevant recommendations, leading to reduced overall engagement with recommended items. To answer this question, we first conducted offline analyses (Study 1) on SuggestBot, a task-routing recommender system for Wikipedia, then did a three-month controlled experiment (Study 2). Our results show that presenting users with articles from underrepresented topics increased the proportion of work done on those articles without significantly reducing overall recommendation uptake. We discuss the implications of our results, including how ignoring the article discovery process can artificially narrow recommendations on peer production platforms.

For peer production communities to be sustainable, they must attract and retain new contributors. Studies have identified social and technical barriers to entry and discovered some potential solutions, but these solutions have typically focused on a single highly successful community, the English Wikipedia, been tested in isolation, and rarely evaluated through controlled experiments. We propose the Newcomer Homepage, a central place where newcomers can learn how peer production works and find opportunities to contribute, as a solution for attracting and retaining newcomers. The homepage was built upon existing research and designed in collaboration with partner communities. Through a large-scale controlled experiment spanning 27 non-English Wikipedia wikis, we evaluate the homepage and find modest gains, and that having a positive effect on the newcomer experience depends on the newcomer's context. We discuss how this impacts interventions that aim to improve the newcomer experience in peer production communities.

Peer production communities have been proven to be successful at creating valuable artefacts, with Wikipedia as a prime example. However, a number of studies have shown that work in these communities tends to be of uneven quality and certain content areas receive more attention than others. In this paper, we examine the efficacy of a range of targeted strategies to increase the quality of under-attended content areas in peer production communities. Mining data from five quality improvement projects in the English Wikipedia, the largest peer production community in the world, we show that certain types of strategies (e.g. creating artefacts from scratch) have better quality outcomes than others (e.g. improving existing artefacts), even if both are done by a similar cohort of participants. We discuss the implications of our findings for Wikipedia as well as other peer production communities.

Session identification is a common strategy used to develop metrics for web analytics and perform behavioral analyses of user-facing systems. Past work has argued that session identification strategies based on an inactivity threshold is inherently arbitrary or has advocated that thresholds be set at about 30 minutes. In this work, we demonstrate a strong regularity in the temporal rhythms of user initiated events across several different domains of online activity (incl. video gaming, search, page views and volunteer contributions). We describe a methodology for identifying clusters of user activity and argue that the regularity with which these activity clusters appear implies a good rule-of-thumb inactivity threshold of about 1 hour. We conclude with implications that these temporal rhythms may have for system design based on our observations and theories of goal-directed human activity.

In this paper we address the problem of developing actionable quality models for Wikipedia, models whose features directly suggest strategies for improving the quality of a given article. We first survey the literature in order to understand the notion of article quality in the context of Wikipedia and existing approaches to automatically assess article quality. We then develop classification models with varying combinations of more or less actionable features, and find that a model that only contains clearly actionable features delivers solid performance. Lastly we discuss the implications of these results in terms of how they can help improve the quality of articles across Wikipedia.