Scientific research increasingly relies on open source software (OSS). Funding OSS development requires intentional focus on issues of scholarly credit, unique forms of labor, maintenance, governance, and inclusive community-building. Such issues cut across different scientific disciplines that make them of interest to a variety of funders and institutions but may present challenges in understanding generalized needs. Here we present 10 simple rules for investing in scientific OSS and the teams who build and maintain it.

Wikipedia is playing an increasingly central role on the web, and the policies its contributors follow when sourcing and fact-checking content affect million of readers. Among these core guiding principles, verifiability policies have a particularly important role. Verifiability requires that information included in a Wikipedia article be corroborated against reliable secondary sources. Because of the manual labor needed to curate Wikipedia at scale, however, its contents do not always evenly comply with these policies. Citations (i.e. reference to external sources) may not conform to verifiability requirements or may be missing altogether, potentially weakening the reliability of specific topic areas of the free encyclopedia. In this paper, we aim to provide an empirical characterization of the reasons why and how Wikipedia cites external sources to comply with its own verifiability guidelines. First, we construct a taxonomy of reasons why inline citations are required, by collecting labeled data from editors of multiple Wikipedia language editions. We then crowdsource a large-scale dataset of Wikipedia sentences annotated with categories derived from this taxonomy. Finally, we design algorithmic models to determine if a statement requires a citation, and to predict the citation reason . We evaluate the accuracy of such models across different classes of Wikipedia articles of varying quality, and on external datasets of claims annotated for fact-checking purposes.

Justine Zhang, Jonathan Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Dario Taraborelli, Nithum Thain. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.

Yiqing Hua, Cristian Danescu-Niculescu-Mizil, Dario Taraborelli, Nithum Thain, Jeffery Sorensen, Lucas Dixon. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.

Donation-based support for open, peer production projects such as Wikipedia is an important mechanism for preserving their integrity and independence. For this reason understanding donation behavior and incentives is crucial in this context. In this work, using a dataset of aggregated donation information from Wikimedia's 2015 fund-raising campaign, representing nearly 1 million pages from English and French language versions of Wikipedia, we explore the relationship between the properties of contents of a page and the number of donations on this page. Our results suggest the existence of a reciprocity mechanism, meaning that articles that provide more utility value attract a higher rate of donation. We discuss these and other findings focusing on the impact they may have on the design of banner-based fundraising campaigns. Our findings shed more light on the mechanisms that lead people to donate to Wikipedia and the relation between properties of contents and donations.

Socialization in online communities allows existing members to welcome and recruit newcomers, introduce them to community norms and practices, and sustain their early participation. However, socializing newcomers does not come for free: in large communities, socialization can result in a significant workload for mentors and is hard to scale. In this study we present results from an experiment that measured the effect of a lightweight socialization tool on the activity and retention of newly registered users attempting to edit for the first time Wikipedia. Wikipedia is struggling with the retention of newcomers and our results indicate that a mechanism to elicit lightweight feedback and to provide early mentoring to newcomers improves their chances of becoming long-term contributors.

Open collaboration communities thrive when participation is plentiful. Recent research has shown that the English Wikipedia community has constructed a vast and accurate information resource primarily through the monumental effort of a relatively small number of active, volunteer editors. Beyond Wikipedia's active editor community is a substantially larger pool of potential participants: readers. In this paper we describe a set of field experiments using the Article Feedback Tool, a system designed to elicit lightweight contributions from Wikipedia's readers. Through the lens of social learning theory and comparisons to related work in open bug tracking software, we evaluate the costs and benefits of the expanded participation model and show both qualitatively and quantitatively that peripheral contributors add value to an open collaboration community as long as the cost of identifying low quality contributions remains low.

Over the last years, Wikipedia has seen an explosion of academic interest, as indicated by a steadily increasing volume of scholarly publications. Due to its history, its size and the immediate availability of its data under open licenses, Wikipedia has served over time as a testbed for sociological and psychological theory; as the primary source of data for models of commons-based peer production and computer-supported collaboration; as a body of norms for research on the governance of online communities; or as a large multilingual corpus to mine, or against which to train text analysis algorithms. This explosion of academic interest reveals a gap between Wikipedia as a topic of scholarly research and Wikipedia as a living community in need of actionable solutions, facing real challenges and the first serious growth and sustainability problem in its entire lifecycle. The Wikimedia Foundation and the Wikimedia communities have yet to find a viable model to leverage academic expertise to solve these challenges, in the same way that Wikimedia projects have effectively engaged with a large community of contributors and software developers to produce its contents and support its open source infrastructure. In this talk I will review recent research trends spanning scholarly work and internal research conducted at the Wikimedia Foundation, and how these relate to some of the most urgent needs of the Wikimedia movement and the Wikimedia Foundation's work priorities. I'll discuss models that can support actionable research, as well as open opportunities for researchers and contributors to collaborate on developing joint solutions and identifying new growth opportunities for WIkipedia and its communities.