Because open source software relies on individuals who select their own tasks, it is often underproduced-a term used by software engineering researchers to describe when a piece of software's relative quality is lower than its relative importance. We examine the social and technical factors associated with underproduction through a comparison of software packaged by the Debian GNU/Linux community. We test a series of hypotheses developed from a reading of prior research in software engineering. Although we find that software age and programming language age offer a partial explanation for variation in underproduction, we were surprised to find that the association between underproduction and package age is weaker at high levels of programming language age. With respect to maintenance efforts, we find that additional resources are not always tied to better outcomes. In particular, having higher numbers of contributors is associated with higher underproduction risk. Also, contrary to our expectations, maintainer turnover and maintenance by a declared team are not associated with lower rates of underproduction. Finally, we find that the people working on bugs in underproduced packages tend to be those who are more central to the community's collaboration network structure, although contributors' betweenness centrality (often associated with brokerage in social networks) is not associated with underproduction.

Communicating about some vital topics---such as sexuality and health---is treated as taboo and subjected to censorship. How can we construct knowledge about these topics? Wikipedia is home to numerous high-quality knowledge artifacts about taboo topics like sexual organs and human reproduction. How did these artifacts come into being? How is their existence sustained? This mixed-methods comparative project builds on previous work on taboo topics in Wikipedia and draws from qualitative and quantitative approaches. We follow a sequential complementary design, developing a narrative articulation of the life of taboo articles, comparing them to nontaboo articles, and examining some of their quantifiable traits. We find that taboo knowledge artifacts develop through multiple successful collaboration styles and, unsurprisingly, that taboo subjects are the sites of conflict. We identify and describe six themes in the development of taboo knowledge artifacts. These artifacts need resilient leadership and engaged organizations to thrive under conditions of limited identifiability and disjointed sensemaking , while contributors simultaneously engage in emergent governance and imagining public audiences . Our observations have important implications for supporting public knowledge work on controversial subjects such as taboos and more generally.

What types of governance arrangements make some self-governed online groups more vulnerable to disinformation campaigns? We present a qualitative comparative analysis of the Croatian and Serbian Wikipedia editions to answer this question. We do so because between at least 2011 and 2020, the Croatian language version of Wikipedia was taken over by a small group of administrators who introduced far-right bias and outright disinformation. Dissenting editorial voices were reverted, banned, and blocked. Although Serbian, Bosnian, and Serbo-Croatian Wikipedias share many linguistic and cultural features, and faced similar threats, they seem to have largely avoided this fate. Based on a grounded theory analysis of interviews with members of these communities and others in cross-functional platform-level roles, we propose that the convergence of three features---high perceived value as a target, limited early bureaucratic openness, and a preference for personalistic, informal forms of organization over formal ones---produced a window of opportunity for governance capture on Croatian Wikipedia. Our findings illustrate that online community governing infrastructures can play a crucial role in systematic disinformation campaigns and other influence operations.

Content moderation practices and technologies need to change over time as requirements and community expectations shift. However, attempts to restructure the existing moderation practices can be difficult, especially for platforms that rely on their communities to moderate, because changes can transform the workflow and workload participants' reward systems. By examining the extensive archival discussions around a prepublication moderation technology on Wikipedia named Flagged Revisions, complemented by seven semi-structured interviews, we identify various challenges in restructuring community-based moderation practices. Thus, we find that while a new system might sound good in theory and perform well in terms of quantitative metrics, it may conflict with existing social norms. Furthermore, our findings underscore how the relationship between platforms and self-governed communities can hinder the ability to assess the performance of any new system and introduce considerable costs related to maintaining, overhauling, or scrapping any piece of infrastructure.

By definition, people are reticent or even unwilling to talk about taboo subjects. Because subjects like sexuality, health, and violence are taboo in most cultures, important information on each of these subjects can be difficult to obtain. Are peer produced knowledge bases like Wikipedia a promising approach for providing people with information on taboo subjects? With its reliance on volunteers who might also be averse to taboo, can the peer production model produce high-quality information on taboo subjects? In this paper, we seek to understand the role of taboo in knowledge bases produced by volunteers. We do so by developing a novel computational approach to identify taboo subjects and by using this method to identify a set of articles on taboo subjects in English Wikipedia. We find that articles on taboo subjects are more popular than non-taboo articles and that they are frequently vandalized. Despite frequent vandalism attacks, we also find that taboo articles are higher quality than non-taboo articles. We hypothesize that stigmatizing societal attitudes will lead contributors to taboo subjects to seek to be less identifiable. Although our results are consistent with this proposal in several ways, we surprisingly find that contributors make themselves more identifiable in others.

Through a mixed-method analysis of data from Scratch, we examine how novices learn to program with simple data structures by using community-produced learning resources. First, we present a qualitative study that describes how community-produced learning resources create archetypes that shape exploration and may disadvantage some with less common interests. In a second quantitative study, we find broad support for this dynamic in several hypothesis tests. Our findings identify a social feedback loop that we argue could limit sources of inspiration, pose barriers to broadening participation, and confine learners’ understanding of general concepts. We conclude by suggesting several approaches that may mitigate these dynamics.

Why do some peer production projects do a better job at engaging potential contributors than others? We address this question by comparing three Indian language Wikipedias, namely, Malayalam, Marathi, and Kannada. We found that although the three projects share goals, technological infrastructure, and a similar set of challenges, Malayalam Wikipedia’s community engages language speakers in contributing at a much higher rate than the others. Drawing from a grounded theory analysis of interviews with 18 community participants from the three projects, we found that experience with participatory governance and free/open-source software in the Malayalam community supported high engagement of contributors. Counterintuitively, we found that financial resources intended to increase participation in the Marathi and Kannada communities hindered the growth of these communities. Our findings underscore the importance of social and cultural context in the trajectories of peer production communities.

Although informal online learning communities have proliferated over the last two decades, a fundamental question remains: What are the users of these communities expected to learn? Guided by the work of Etienne Wenger on communities of practice, we identify three distinct types of learning goals common to online informal learning communities: the development of domain skills, the development of identity as a community member, and the development of community-specific values and practices. Given these goals, what is the best way to support learning? Drawing from previous research in social computing, we ask how different types of legitimate peripheral participation by newcomers-contribution to core tasks, engagement with practice proxies, social bonding, and feedback exchange-may be associated with these three learning goals. Using data from the Scratch online community, we conduct a quantitative analysis to explore these questions. Our study contributes both theoretical insights and empirical evidence on how different types of learning occur in informal online environments.

Large-scale quantitative analyses have shown that individuals frequently talk to each other about similar things in different online spaces. Why do these overlapping communities exist? We provide an answer grounded in the analysis of 20 interviews with active participants in clusters of highly related subreddits. Within a broad topical area, there are a diversity of benefits an online community can confer. These include (a) specific information and discussion, (b) socialization with similar others, and (c) attention from the largest possible audience. A single community cannot meet all three needs. Our findings suggest that topical areas within an online community platform tend to become populated by groups of specialized communities with diverse sizes, topical boundaries, and rules. Compared with any single community, such systems of overlapping communities are able to provide a greater range of benefits.

Many online communities rely on postpublication moderation where contributors-even those that are perceived as being risky-are allowed to publish material immediately and where moderation takes place after the fact. An alternative arrangement involves moderating content before publication. A range of communities have argued against prepublication moderation by suggesting that it makes contributing less enjoyable for new members and that it will distract established community members with extra moderation work. We present an empirical analysis of the effects of a prepublication moderation system called FlaggedRevs that was deployed by several Wikipedia language editions. We used panel data from 17 large Wikipedia editions to test a series of hypotheses related to the effect of the system on activity levels and contribution quality. We found that the system was very effective at keeping low-quality contributions from ever becoming visible. Although there is some evidence that the system discouraged participation among users without accounts, our analysis suggests that the system's effects on contribution volume and quality were moderate at most. Our findings imply that concerns regarding the major negative effects of prepublication moderation systems on contribution quality and project productivity may be overstated.

The widespread adoption of Free/Libre and Open Source Software (FLOSS) means that the ongoing maintenance of many widely used software components relies on the collaborative effort of volunteers who set their own priorities and choose their own tasks. We argue that this has created a new form of risk that we call ‘underproduction' which occurs when the supply of software engineering labor becomes out of alignment with the demand of people who rely on the software produced. We present a conceptual framework for identifying relative underproduction in software as well as a statistical method for applying our framework to a comprehensive dataset from the Debian GNU/Linux distribution that includes 21,902 source packages and the full history of 461,656 bugs. We draw on this application to present two experiments: (1) a demonstration of how our technique can be used to identify at-risk software packages in a large FLOSS repository and (2) a validation of these results using an alternate indicator of package risk. Our analysis demonstrates both the utility of our approach and reveals the existence of widespread underproduction in a range of widely-installed software components in Debian.

Online community moderators often rely on social signals such as whether or not a user has an account or a profile page as clues that users may cause problems. Reliance on these clues can lead to "overprofiling'' bias when moderators focus on these signals but overlook the misbehavior of others. We propose that algorithmic flagging systems deployed to improve the efficiency of moderation work can also make moderation actions more fair to these users by reducing reliance on social signals and making norm violations by everyone else more visible. We analyze moderator behavior in Wikipedia as mediated by RCFilters, a system which displays social signals and algorithmic flags, and estimate the causal effect of being flagged on moderator actions. We show that algorithmically flagged edits are reverted more often, especially those by established editors with positive social signals, and that flagging decreases the likelihood that moderation actions will be undone. Our results suggest that algorithmic flagging systems can lead to increased fairness in some contexts but that the relationship is complex and contingent.

User-generated content sites routinely block contributions from users of privacy-enhancing proxies like Tor because of a perception that proxies are a source of vandalism, spam, and abuse. Although these blocks might be effective, collateral damage in the form of unrealized valuable contributions from anonymity seekers is invisible. One of the largest and most important user-generated content sites, Wikipedia, has attempted to block contributions from Tor users since as early as 2005. We demonstrate that these blocks have been imperfect and that thousands of attempts to edit on Wikipedia through Tor have been successful. We draw upon several data sources and analytical techniques to measure and describe the history of Tor editing on Wikipedia over time and to compare contributions from Tor users to those from other groups of Wikipedia users. Our analysis suggests that although Tor users who slip through Wikipedia's ban contribute content that is more likely to be reverted and to revert others, their contributions are otherwise similar in quality to those from other unregistered participants and to the initial contributions of registered users.

Anonymity can enable both healthy online interactions like support-seeking and toxic behaviors like hate speech. How do online service providers balance these threats and opportunities? This two-part qualitative study examines the challenges perceived by open collaboration service providers in allowing anonymous contributions to their projects. We interviewed eleven people familiar with organizational decisions related to privacy and security at five open collaboration projects and followed up with an analysis of public discussions about anonymous contribution to Wikipedia. We contrast our findings with prior work on threats perceived by project volunteers and explore misalignment between policies aiming to serve contributors and the privacy practices of contributors themselves.

By choice or by necessity, some contributors to commons-based peer production sites use privacy-protecting services to remain anonymous. As anonymity seekers, users of the Tor network have been cast both as ill-intentioned vandals and as vulnerable populations concerned with their privacy. In this study, we use a dataset drawn from a corpus of Tor edits to Wikipedia to uncover the character of Tor users' contributions. We build in-depth narrative descriptions of Tor users' actions and conduct a thematic analysis that places their editing activity into seven broad groups. We find that although their use of a privacy-protecting service marks them as unusual within Wikipedia, the character of many Tor users' contributions is in line with the expectations and norms of Wikipedia. However, our themes point to several important places where lack of trust promotes disorder, and to contributions where risks to contributors, service providers, and communities are unaligned.

Management of technological change in organizations is one of the most enduring topics in the literature on computer-supported cooperative work. The successful navigation of technological change is both more challenging and more critical in online communities that are entirely mediated by technology than it is in traditional organizations. This paper presents an analysis of 14 in-depth interviews with moderators of subcommunities of one technological platform (Reddit) that added communities on a new technological platform (Discord). Moderation teams experienced several problems related to moderating content at scale as well as a disconnect between the affordances of Discord and their assumptions based on their experiences on Reddit. We found that moderation teams used Discord's API to create scripts and bots that augmented Discord to make the platform work more like tools on Reddit. These tools were particularly important in communities struggling with scale. Our findings suggest that increasingly widespread end user programming allow users of social computing systems to innovate and deploy solutions to unanticipated design problems by transforming new technological platforms to align with their past expectations.

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.

A core aim for designing constructionist learning systems and toolkits is enabling "wide walls"-a metaphor used to describe supporting a diverse range of creative outcomes. Ensuring that a broad design space is afforded to learners by a toolkit is a common approach to achieving wide walls. We use econometric methods to provide an empirical test of the wide walls theory through a natural experiment in the Scratch online community. We estimate the causal effect of a policy change that gave a large number of Scratch users access to a more powerful version of Scratch data structures, effectively widening the walls for learners. We show that access to and use of these more powerful new data structures caused learners to use data structures more frequently. Our findings provide support for the theory that wide walls can increase engagement and learning.

Do patterns of growth and stabilization found in large peer production systems such as Wikipedia occur in other communities? This study assesses the generalizability of Halfaker et al.'s influential 2013 paper on "The Rise and Decline of an Open Collaboration System." We replicate its tests of several theories related to newcomer retention and norm entrenchment using a dataset of hundreds of active peer production wikis from Wikia. We reproduce the subset of the findings from Halfaker and colleagues that we are able to test, comparing both the estimated signs and magnitudes of our models. Our results support the external validity of Halfaker et al.'s claims that quality control systems may limit the growth of peer production communities by deterring new contributors and that norms tend to become entrenched over time.

Although informal online learning communities are made possible by users' decisions to share their creations, participation by females and other marginalized groups remains stubbornly low in technical communities. Using descriptive statistics and a unique dataset of shared and unshared projects from over 1.1 million users of Scratch-a collaborative programming community for young people-we show that while girls share less initially, this trend flips among experienced users. Using Bayesian regression analyses, we show that this relationship can largely be attributed to differences in the way boys and girls participate. We also find that while prior positive feedback is correlated with increased sharing among inexperienced users, this effect also reverses with experience or with the addition of controls. Our findings provide a description of the dynamics behind online learners' decisions to share, open new research questions, and point to several lessons for system designers.

Research in social computing has typically conceptualized community growth as a process through which a group welcomes newcomers individually. However, online communities also grow through formal and informal mergers, where groups of newcomers with shared experiences join in batches. To understand this process, we conducted a six month, comparative ethnography of two mergers of World of Warcraft raid guilds. While one merger led to a single, thriving community, the other led to the dissolution of both pre-merger groups. Analysis of our ethnographic data suggests that differences in managing organizational culture (a concept drawn from organization studies) led the successful and failed cases to diverge. The study contributes to our understanding of why some attempts to integrate members of different communities are more successful than others. We outline several ways that community leaders, researchers, and designers can effectively take organizational culture into account.

In this paper, we present Scratch Community Blocks, a new system that enables children to programmatically access, analyze, and visualize data about their participation in Scratch, an online community for learning computer programming. At its core, our approach involves a shift in who analyzes data: from adult data scientists to young learners themselves. We first introduce the goals and design of the system and then demonstrate it by describing example projects that illustrate its functionality. Next, we show through a series of case studies how the system engages children in not only representing data and answering questions with data but also in self-reflection about their own learning and participation.

As contemporary youth learn, play, and socialize online, their activities are often being recorded and analyzed. What should young people know about these data collection and analysis efforts? Although critiques of these new forms of data collection and analysis have grown increasingly loud, the voices of users, and particularly youth, have largely been absent. This paper explores the critical perspectives of youth who are programming with public data about their own learning and social interaction in the Scratch online community. Using a bottom-up approach based on ethnographic observation of discussions among these young users, we identify a series of themes in how these youth critique, question, and debate the implications of data analytics. We connect these themes-framed in terms of critical data literacies-to expert critiques and discuss the implications of these findings for education and design.

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.

Education research suggests that learning in one's local language can have a positive impact on learning outcomes. We offer a quantitative test of the association between local language use and the rate at which youth learn to program. Using longitudinal data drawn from five countries and over 15,000 users of Scratch, a large informal learning community, we find that novice users who code with their programming language keywords and environment localized into their home countries' primary language demonstrate new programming concepts at a faster rate than users from the same countries whose interface is in English. We conclude with a discussion of the implications of our findings for designers of online learning systems.

Quantitative studies of learning using block-based programming languages in informal environments have relied on identifying the presence or absence of individual visual blocks in learners' projects. Many important programming concepts (e.g., initializing a variable) involve the combination of several blocks. In this poster, we present a technique that uses a statistical method from epidemiology called "survival analysis" to model the rate at which programmers begin to use new code patterns. By analyzing data drawn from the trajectories of over 90,000 users from the Scratch online community, we demonstrate the potential of our approach. In particular, we model when users are at higher and lower levels of "risk" of demonstrating two particular code patterns -- variable initialization and counting collisions. We show that learning of these patterns is associated with behaviors like viewing the source code of other projects, remixing, and commenting. We explain how our method can be extended to help understand predictors of skill acquisition in informal environments more generally and how it can inform the design of more effective learning support structures.

Although Couchsurfing and Airbnb are both online communities that help users host strangers in their homes, they differ in an important sense: Couchsurfing prohibits monetary payment while Airbnb is built around it.We conducted interviews with users experienced on both Couchsurfing and Airbnb ("dual-users") to better understand systemic differences between the platforms. Based on these interviews we propose that, compared to Couchsurfing, Airbnb: (1) appears to require higher quality services, (2) places more emphasis on places over people, and (3) shifts social power from hosts to guests. Using public profiles from both platforms, we present analyses exploring each theme. Finally, we present evidence showing that Airbnb's growth has coincided with a decline in Couchsurfing. Taken together, our findings paint a complex picture of the changing character of network hospitality.

We present a qualitative analysis of interviews with participants in the NoSleep community within Reddit where millions of fans and writers of horror fiction congregate. We explore how the community handled a massive, sudden, and sustained increase in new members. Although existing theory and stories like Usenet's infamous "Eternal September" suggest that large influxes of newcomers can hurt online communities, our interviews suggest that NoSleep survived without major incident. We propose that three features of NoSleep allowed it to manage the rapid influx of newcomers gracefully: (1) an active and well-coordinated group of administrators, (2) a shared sense of community which facilitated community moderation, and (3) technological systems that mitigated norm violations. We also point to several important trade-offs and limitations.

This paper contributes to a growing body of work that attempts to measure informal learning online by revisiting two of the most surprising findings from a 2012 study on skill progression in Scratch by Scaffidi and Chambers: users tend to share decreasingly code-heavy projects over time; and users' projects trend toward using a less diverse range of code concepts. We revisit Scaffidi and Chambers's work in three ways: with a replication of their study using the full population of projects from which they sampled, a simulation study that replicates both their analytic and sampling methodology, and an alternative analysis that addresses several important threats. Our results suggest that the population estimates are opposite in sign to those presented in the original work.

Theorists and advocates of “remixing” have suggested that appropriation can act as a pathway for learning. We test this theory quantitatively using data from more than 2.4 million multimedia programming projects shared by more than 1 million users in the Scratch online community. First, we show that users who remix more often have larger repertoires of programming commands even after controlling for the numbers of projects and amount of code shared. Second, we show that exposure to computational thinking concepts through remixing is associated with increased likelihood of using those concepts. Our results support theories that young people learn through remixing, and have important implications for designers of social computing systems.

E-petitioning has become one of the most important and popular forms of online activism. Although e-petition success is driven by user behavior, users have received relatively little study by HCI and social computing researchers. Drawing from theoretical and empirical work in analogous social computing systems, we identify two potentially competing theories about the trajectories of users in e-petition platforms: (1) "power" users in social computing systems are born, not made; and (2) users mature into "power" users. In a quantitative analysis of data from Change.org, one of the largest online e-petition platforms, we test and find support for both theories. A follow-up qualitative analysis shows that not only do users learn from their experience, systems also "learn" from users to make better recommendations. In this sense, we find that although power users are "born," they are also "made" through both processes of personal growth and improved support from the system.

Page protection is a feature of wiki software that allows administrators to restrict contributions to particular pages. For example, pages are frequently protected so that they can only be edited by administrators. Page protection affects tens of thousands of pages in English Wikipedia and renders many of Wikipedia's most visible pages uneditable by the vast majority of visitors. That said, page protection has attracted very little attention and is rarely taken into account by researchers. This note describes page protection and illustrates why it plays an important role in shaping user behavior on wikis. We also present a new longitudinal dataset of page protection events for English Wikipedia, the software used to produce it, and results from tests that support both the validity of the dataset and the impact of page protection on patterns of editing.

Redirects are special pages in wikis that silently transport visitors to other pages. Although redirects make up a majority of all article pages in English Wikipedia, they have attracted very little attention and are rarely taken into account by researchers. This note describes redirects and illustrates why they play an important role in shaping activity in Wikipedia. We also present a novel longitudinal dataset of redirects for English Wikipedia and the software used to produce it. Using this dataset, we revisit several important published findings about Wikipedia to show that accounting for redirects can have important effects on research.

In this paper, we use evidence from a remixing community to evaluate two pieces of common wisdom about collaboration. First, we test the theory that jointly produced works tend to be of higher quality than individually authored products. Second, we test the theory that collaboration improves the quality of functional works like code, but that it works less well for artistic works like images and sounds. We use data from Scratch, a large online community where hundreds of thousands of young users share and remix millions of animations and interactive games. Using peer-ratings as a measure of quality, we estimate a series of fitted regression models and find that collaborative Scratch projects tend to receive ratings that are lower than individually authored works. We also find that code-intensive collaborations are rated higher than media-intensive efforts. We conclude by discussing the limitations and implications of these findings.

In this paper, we explore the role that attribution plays in shaping user reactions to content reuse, or remixing, in a large user-generated content community. We present two studies using data from the Scratch online community - a social media platform where hundreds of thousands of young people share and remix animations and video games. First, we present a quantitative analysis that examines the effects of a technological design intervention introducing automated attribution of remixes on users' reactions to being remixed. We compare this analysis to a parallel examination of "manual" credit-giving. Second, we present a qualitative analysis of twelve in-depth, semi-structured, interviews with Scratch participants on the subject of remixing and attribution. Results from both studies suggest that automatic attribution done by technological systems (i.e., the listing of names of contributors) plays a role that is distinct from, and less valuable than, credit which may superficially involve identical information but takes on new meaning when it is given by a human remixer. We discuss the implications of these findings for the designers of online communities and social media platforms.

This paper examines the distribution, adoption, and evolution of an open-source toolkit we developed called the LilyPad Arduino. We track the two-year history of the kit and its user community from the time the kit was commercially introduced, in October of 2007, to November of 2009. Using sales data, publicly available project documentation and surveys, we explore the relationship between the LilyPad and its adopters. We investigate the community of developers who has adopted the kit---paying special attention to gender---explore what people are building with it, describe how user feedback impacted the development of the kit and examine how and why people are contributing their own LilyPad-inspired tools back to the community. What emerges is a portrait of a new technology and a new engineering/design community in co-evolution.