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.

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.

Short videos on social media are the dominant way young people consume content. News outlets aim to reach audiences through news reels—short videos conveying news—but struggle to translate traditional journalistic formats into short, entertaining videos. To translate news into social media reels, we support journalists in reframing the narrative. In literature, narrative framing is a high-level structure that shapes the overall presentation of a story. We identified three narrative framings for reels that adapt social media norms but preserve news value, each with a different balance of information and entertainment. We introduce ReelFramer, a human-AI co-creative system that helps journalists translate print articles into scripts and storyboards. ReelFramer supports exploring multiple narrative framings to find one appropriate to the story. AI suggests foundational narrative details, including characters, plot, setting, and key information. ReelFramer also supports visual framing; AI suggests character and visual detail designs before generating a full storyboard. Our studies show that narrative framing introduces the necessary diversity to translate various articles into reels, and establishing foundational details helps generate scripts that are more relevant and coherent. We also discuss the benefits of using narrative framing and foundational details in content retargeting.

We identify and describe episodes of sensemaking around challenges in modern Artificial-Intelligence (AI)-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI’s inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems.

News media often leverage documents to find ideas for stories, while being critical of the frames and narratives present. Developing angles from a document such as a press release is a cognitively taxing process, in which journalists critically examine the implicit meaning of its claims. Informed by interviews with journalists, we developed AngleKindling, an interactive tool which employs the common sense reasoning of large language models to help journalists explore angles for reporting on a press release. In a study with 12 professional journalists, we show that participants found AngleKindling significantly more helpful and less mentally demanding to use for brainstorming ideas, compared to a prior journalistic angle ideation tool. AngleKindling helped journalists deeply engage with the press release and recognize angles that were useful for multiple types of stories. From our findings, we discuss how to help journalists customize and identify promising angles, and extending AngleKindling to other knowledge-work domains.

Data science teams working on a shared analysis face coordination problems such as dividing up the work to be done, monitoring performance and integrating the pieces. Research on distributed software development teams has raised the potential of stigmergic coordination, that is, coordination through a shared work product in place of explicit communication. The MIDST system was developed to support stigmergic coordination by making individual contributions to a shared work product visible, legible and combinable. In this paper, we present initial studies of a total of 40 student teams (24 using MIDST) that shows that teams that used MIDST did experience the intended system affordances to support their work, did seem to coordinate at least in part stigmergically and performed better on an assigned project.

The increased pervasiveness of technological advancements in automation makes it urgent to address the question of how work is changing in response. Focusing on applications of machine learning (ML) to automate information tasks, we draw on a simple framework for identifying the impacts of an automated system on a task that suggests 3 patterns for the use of ML-decision support, blended decision making and complete automation. In this paper, we extend this framework by considering how automation of one task might have implications for interdependent tasks and how automation applies to coordination mechanisms.

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

Virtual collaborations bring together people who must work together despite having varied access to and understanding of the work at hand. In many cases, the collaborations are technology supported, meaning that the work is done through shared documents. We develop a framework articulating the characteristics of documents supporting collaborators with access to asymmetric knowledge versus those with access to symmetric knowledge. Drawing on theories about document genre, boundary objects, and provenance, we hypothesize that documents supporting asymmetric collaborators are likely to articulate or prescribe their own (a) purpose, (b) context of use, (c) content and form, and (d) provenance in greater detail than documents supporting symmetric collaborators. We explore these hypotheses through content analysis of documents and instructions for documents from a variety of free/libre open‐source projects (FLOSS). We present findings consistent with the hypotheses developed as well as results extending beyond our theory‐derived assumptions. When participants have access to the same knowledge, the study suggests that prescriptions about the content of documents become less important compared with prescriptions about the context, provenance, and process of work. The study contributes with a dynamic perspective on communicative practices that consider an often‐uneven distribution of knowledge in virtual collaborations.

We present a conceptual framework for socio-technical affordances for stigmergic coordination, that is, coordination supported by a shared work product. Based on research on free/libre open source software development, we theorize that stigmergic coordination depends on three sets of socio-technical affordances: the visibility and combinability of the work, along with defined genres of work contributions. As a demonstration of the utility of the developed framework, we use it as the basis for the design and implementation of a system, MIDST, that supports these affordances and that we thus expect to support stigmergic coordination. We describe an initial assessment of the impact of the tool on the work of project teams of three to six data-science students that suggests that the tool was useful but also in need of further development. We conclude with plans for future research and an assessment of theory-driven system design.

This all-day workshop aims to promote convergence among its participants on research related to working with intelligent machines. We define intelligent machines as both material (e.g., robots) and immaterial (e.g., algorithms) computing technologies that can be characterized by autonomy, the ability to learn, and the ability to interact with other systems and with humans. The workshop has three goals: identifying specific research problems around work and intelligent machines, developing a common language base that can facilitate interdisciplinary collaboration among researchers, and identifying information and cyber-infrastructure needs to support convergent research. Workshop activities will facilitate interdisciplinary dialogue and strive to generate high-impact research ideas to advance each of these goals.

We look for evidence of stigmergic coordination (i.e., coordination mediated by changes to a shared work product) in the context of Wikipedia. Using a novel approach to identifying edits to the same part of a Wikipedia article, we show that a majority of edits to two example articles are not associated with discussion on the article Talk page, suggesting the possibility of stigmergic coordination. However, discussion does seem to be related to article quality, suggesting the limits to this approach to coordination.

User-generated content (UGC) projects involve large numbers of mostly unpaid contributors collaborating to create content. Motivation for such contributions has been an active area of research. In prior research, motivation for contribution to UGC has been considered a single, static and individual phenomenon. In this paper, we argue that it is instead three separate but interrelated phenomena. Using the theory of helping behaviour as a framework and integrating social movement theory, we propose a stage theory that distinguishes three separate sets (initial, sustained and meta) of motivations for participation in UGC. We test this theory using a data set from a Wikimedia Editor Survey (Wikimedia Foundation, 2011). The results suggest several opportunities for further refinement of the theory but provide support for the main hypothesis, that different stages of contribution have distinct motives. The theory has implications for both researchers and practitioners who manage UGC projects.

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.

We consider the processes of achieving alignment in coordinated inter-organizational networks through a case study of a system development project in ARC Transistance, a network of European automobile clubs that cooperate to provide pan-European service. The theoretical contribution of the paper is, first, an extended strategic alignment model for inter-organizational networks that distinguishes between integration of IS with business strategy and infrastructure, and what we label ‘accordance’ between the strategies and infrastructures of the network and the member firms. Second, we propose that for a network organization, network and member strategies might be complementary as well as tightly coupled. We similarly argue that IS architectures for networks should strive for being ‘business strategy-neutral’ to more easily accommodate the diversity of members. Finally, we discuss how the process of developing a network information system can be a driver towards network alignment, but how the lack of effective governance structures makes alignment harder to achieve.

We evaluate the efficiency of an online knowledge production project and identify factors that affect efficiency. To assess efficiency, we used the Data Envelopment Analysis (DEA) modelling methodology. We apply DEA to data from more than 30 Wikipedia language projects over three years. We show that the main Wikipedia projects were indeed less efficient that smaller ones, an effect that can be attributed in part to decreasing returns to scale.

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.

Citizen science is a form of social computation where members of the public are recruited to contribute to scientific investigations. Citizen-science projects often use web-based systems to support collaborative scientific activities, making them a form of computer-supported cooperative work. However, finding ways to attract participants and confirm the veracity of the data they produce are key issues in making such systems successful. We describe a series of web-based tools and games currently under development to support taxonomic classification of organisms in photographs collected by citizen-science projects. In the design science tradition, the systems are purpose-built to test hypotheses about participant motivation and techniques for ensuring data quality. Findings from preliminary evaluation and the design process itself are discussed.

Free/Libre open source software (FLOSS, e.g., Linux or Apache) is primarily developed by distributed teams. Developers contribute from around the world and coordinate their activity almost exclusively by means of email and bulletin boards, yet some how profit from the advantages and evade the challenges of distributed software development. In this article we investigate the structure and the coordination practices adopted by development teams during the bug-fixing process, which is considered one of main areas of FLOSS project success. In particular, based on a codification of the messages recorded in the bug tracking system of four projects, we identify the accomplished tasks, the adopted coordination mechanisms, and the role undertaken by both the FLOSS development team and the FLOSS community. We conclude with suggestions for further research.

"Appears in Proceedings of the Conference on Computer Supported Cooperative Work, Los Angeles, California, October, 1990."

The Information Lens provides electronic mail users with the ability to write rules that automatically sort, select, and filter their messages. This paper describes preliminary results from an eighteen-month investigation of the use of this system at a corporate test site. We report the experiences of 13 voluntary users who have each had at least three months experience with the most recent version of the system. We found that:

Many researchers have investigated and speculated about the link between information technology and organizational structure with very mixed results. This article suggests that part of the reason for these mixed results is the coarseness of previous analyses of both technology and structure. It describes a new and much more detailed perspective for investigating this link. Using concepts of object-oriented programming from artificial intelligence, the information processing that occurs in organizations is characterized in terms of the kinds of messages people exchange and the ways they process those messages. The utility of this approach is demonstrated through the analysis of a case in which a reduction in levels of management is coupled with the introduction of a computer conferencing system. The detailed model developed for this case helps explain both macro-level data about the changes in the organizational structure, and micro-level data about individuals' use of the system.

Many researchers have investigated and speculated about the link between information technology and organizational structure with very mixed results. This paper suggests that part of the reason for these mixed results is the coarseness of previous analyses of both technology and structure. The paper describes a new and much more detailed perspective for investigating this link. Using concepts of object-oriented programming from artificial intelligence, the information processing that occurs in organizations is characterized in terms of the kinds of messages people exchange and the ways they process those messages. The utility of this approach is demonstrated through the analysis of a case in which a reduction in levels of management is coupled with the introduction of a computer conferencing system. The detailed model developed for this case helps explain both macro-level data about thd changes in the organizational structure, and micro-level data about individuals' use of the system.