Abstract Members of cooperative groups can work together more effectively if they develop a shared classification schema, but distributed groups face barriers to doing so. To better understand how classifications and classification practices can emerge and support the work of distributed groups, we review the literature on folksonomies (a kind of shared classification schema) in crowdsourcing projects (one type of distributed work). The review yields three potentially productive tensions associated with the development of folksonomies in crowdsourcing projects. First, projects must establish who has the authority to decide on adopted terminology and with what consequences. Second, there can be tension if people who tag objects have different interests in tagging than those who use the tags to search for content. Finally, projects must decide when to intervene to maintain a balance between a stable vocabulary and the ability of the project to accommodate ongoing changes. We illustrate these tensions by comparing how they are handled in the photo-sharing site Flickr, the story-sharing site Archive of Our Own (AO3), the internet culture classification site Know Your Meme, and the citizen science project Gravity Spy. The comparison suggests guidelines for project managers regarding the identified tensions.

As AI is increasingly used in public services, concerns have grown about bias and misalignment with community values. To investigate how ordinary citizens reason about fairness in public-sector AI, we conducted a scenario-based study with 110 participants who evaluated 13 algorithmically-informed government decision systems. Drawing on their responses, we analyze how auditors evaluate risks, identify impacted groups, and navigate fairness trade-offs. Our findings reveal that participants do not audit arbitrarily; they adjust their evaluations to context, reason from values, and exhibit principled subjectivity by applying fairness orientations consistently while remaining sensitive to scenario-specific details. However, we also observe tensions between bias recognition and prioritization, suggesting a gap between awareness and actionable design focus. Building on literature in participatory AI, algorithmic impact assessments, and user-driven auditing, we argue that good auditors are not merely detectors of error but normative agents who surface latent value tensions. We conclude by offering design implications for audit tooling that supports individual and group deliberation, trade-off reasoning, and equity-centered decision-making in the oversight of public algorithms.

Socialization is crucial for open collaboration projects as incumbent participants establish and build relationships within their community. However, socialization often excludes engagement in a group's communicative and language practices. This study examines the socialization of newcomers to the communicative and language practices of Gravity Spy. Participants classify image subjects in Gravity Spy to isolate noise signals in gravitational wave detectors. Analyzing the discussion posts of Gravity Spy newcomers, we determine how they assimilate into the group's communicative practices. We find that socialization in a group’s language practices can be a valuable metric for integration. Our results suggest successful socialization is marked by the convergence of newcomers’ language with the community, influencing retention and participation. These findings provide insights into the linguistic dynamics of virtual citizen science communities and propose strategies for enhancing newcomer integration through language adoption.

We explore patterns of interaction with different learning resources (e.g., forums) to predict learning outcomes in an online citizen science project called Gravity Spy. To explore how volunteers engage with and benefit from these resources, we categorize them based on Sørensen's three forms of presence in learning environments: authority-subject, agent-centered, and communal presence. Methodologically, we apply sequence analysis to traces of volunteer interactions with the project to identify engagement patterns with these resources that predict learning. Our interpretation of these patterns is augmented by insights gleaned from interviews with volunteers about their work and use of learning resources. We find that early in the project, volunteers have only a simple task to learn, and completing that task is most predictive of their learning. At more advanced levels, when tasks become more complex, discussions with other volunteers become increasingly important, and interaction patterns become more varied. Viewing learning as a series of routines allows us to articulate precisely how and in what context learning occurs. We conclude by discussing the implications of these findings for designing citizen science projects that promote learning.

While much research has examined motivations for contributing to citizen science projects, few studies have considered the role of personal values in directing citizen scientists' interactions and contribution patterns. We investigated whether personal values systematically influence the behaviors of individuals who use the Zooniverse platform to select and contribute to citizen science projects. In this paper, we present the results of a research study where we launched a large-scale survey (N = 2,605) to capture personal values using Schwartz's Portrait Values Questionnaire (PVQ-21). We also extracted system log data from participant interactions on Zooniverse. Our results align with previous research suggesting intrinsic type motivators and values tend to drive specific modes of interaction, e.g., exploring projects in different disciplines. We also see that interaction in social spaces, e.g., discussion boards, is driven by values with a personal focus (e.g., self-enhancement) and social focus. Given these results, we provide several suggestions for managing these and similar projects.

Peer production projects involve people in many tasks, from editing articles to analyzing datasets. To facilitate mastery of these practices, projects offer a number of learning resources, ranging from project-defined FAQsto individually-oriented search tools and communal discussion boards. However, it is not clear which project resources best support participant learning, overall and at different stages of engagement. We draw onSørensen's framework of forms of presence to distinguish three types of engagement with learning resources:authoritative, agent-centered and communal. We assigned resources from the Gravity Spy citizen-science into these three categories and analyzed trace data recording interactions with resources using a mixed-effects logistic regression with volunteer performance as an outcome variable. The findings suggest that engagement with authoritative resources (e.g., those constructed by project organizers) facilitates performance initially. However, as tasks become more difficult, volunteers seek and benefit from engagement with their own agent-centered resources and community-generated resources. These findings suggest a broader scope for the design of learning resources for peer production

Researchers studying user behaviors in online communities often conduct analyses of user interaction data recorded in system logs e.g., an edit in Wikipedia. Such analysis relies on collating interactions by a unique identifier such as a user ID. However, if users can contribute without being logged-in (i.e., anonymously) analysis of interaction data omit part of a user's experience. Problematically, anonymous traces are unlikely to be randomly distributed, so their omission can change statistical conclusions, with implications for both research and practice. To understand the impacts on conclusions of leaving out anonymous traces, we conducted an analysis of system logs from two online citizen science projects. Attributing anonymous traces with user IDs, we found that (1) many users contribute anonymously, though with varied patterns; and (2) attributing anonymous traces diminishes empirical evidence used to support theory and change the results of system algorithms. These results suggest anonymous traces have implications for research on user behaviors and the practices associated with using such data to tailor user experiences in online communities.

Research on newcomer roles in peer production sites (e.g., Wikipedia) is characterized by a broad and relatively well articulated set of functionally and culturally recognizable roles. But not all communities come with well-defined roles that newcomers can aspire to occupy. The present study explores activity clusters newcomers create when faced with few recognizable roles to fill and limited access to other participants' work that serves as an exemplar. Drawing on a mixed method research design, we present findings from an analysis of 1,687 newcomers' sessions in an online citizen science project. Our analysis revealed three major findings: (1) newcomers' activities exists across six session types; (2) newcomers toggle between light work sessions and more involved types of production or community engagement; (3) high-level contributors contribute large volumes of work but comment very little and another group contributes large volumes of comments, but works very little. The former group draws heavily on posts contributed by the latter group. Identifying shifts and regularities in contribution facilitate improved mechanisms for engaging participants and for the design of online citizen science communities.

In this paper, we describe the results of an online field experiment examining the impacts of messaging about task novelty on the volume of volunteers' contributions to an online citizen science project. Encouraging volunteers to provide a little more content as they work is an attractive strategy to increase the community's output. Prior research found that an important motivation for participation in online citizen science is the wonder of being the first person to observe a particular image. To appeal to this motivation, a pop-up message was added to an online citizen science project that alerted volunteers when they were the first to annotate a particular image. Our analysis reveals that new volunteers who saw these messages increased the volume of annotations they contributed. The results of our study suggest an additional strategy to increase the amount of work volunteers contribute to online communities and citizen science projects specifically.

Making visible the process of user participation in online crowdsourced initiatives has been shown to help new users understand the norms of participation [2]. However, in many settings, participants lack full access to others' work. Merging the theory of legitimate peripheral participation [18] with Erickson and Kellogg's theory of social translucence [10, 11, 16] we introduce the concept of practice proxies: traces of user participation in online environments that act as resources to orient newcomers towards the norms of practice. Through a combination of virtual [14] and trace ethnography [12] we explore how new users in two online citizen science projects engage with these traces of practice as a way of compensating for a lack of access to the process of the work itself. Our findings suggest that newcomers seek out practice proxies in the social features of the projects that highlight contextualized and specific characteristics of primary work practice.