Numerous social movements (SMs) around the world help support the UN's Sustainable Development Goals (SDGs). Understanding how key events shape SMs is key to the achievement of the SDGs. We have developed SMART (Social Media Analysis & Reasoning Tool) to track social movements related to the SDGs. SMART was designed by a multidisciplinary team of AI researchers, journalists, communications scholars and legal experts. This paper describes SMART's transformer-based multivariate time series Discourse Evolution Engine for Predictions about Social Movements (DEEP) to predict the volume of future articles/posts and the emotions expressed. DEEP outputs probabilistic forecasts with uncertainty estimates, providing critical support for editorial planning and strategic decision-making. We evaluate DEEP with a case study of the #MeToo movement by creating a novel longitudinal dataset (433K Reddit posts and 121K news articles) from September 2024 to June 2025, which is publicly available for research purposes.

Decentralizing the governance of social computing systems to communities promises to empower them to make independent decisions, with nuance and in context. Yet, communities do not govern in isolation. Many problems communities face are common, or move across their boundaries. We propose designing for inter-community governance: mechanisms that support relationships between communities toward coordinating on governance issues. Drawing from workshops with 24 individuals on decentralized, community-run social media, we present six challenges in designing for inter-community governance surfaced through ideas discussed in workshops. These ideas come together as an ecosystem of resources and tools that highlight three key principles for design: modularity, forkability, and polycentricity. We end with a discussion of how workshop ideas might be implemented in future work aiming to support community governance in social computing more broadly.

Online communities often overlap and coexist, despite incongruent norms and approaches to content moderation. When communities diverge, decentralized and federated communities may pursue group-level sanctions, including defederation (disconnection) to block communication between members of specific communities. We investigate the effects of defederation in the context of the Fediverse, a set of decentralized, interconnected social networks with independent governance. Mastodon and Pleroma, the most popular software powering the Fediverse, allow administrators on one server to defederate from another. We use a difference-in-differences approach and matched controls to estimate the effects of defederation events on participation and message toxicity among affected members of the blocked and blocking servers. We find that defederation causes a drop in activity for accounts on the blocked servers, but not on the blocking servers. Also, we find no evidence of an effect of defederation on message toxicity.

Bots have become critical for managing online communities on platforms, especially to match the increasing technical sophistication of online harms. However, community leaders often adoptthird-party bots, creating room for misalignment in their assumptions, expectations, and understandings (i.e., their technological frames) about them. On platforms where sharing bots can be extremely valuable, how community leaders can revise their frames about bots to more effectively adopt them is unclear. In this work, we conducted a qualitative interview study with 16 community leaders on Discord examining how they adopt third-party bots. We found that participants addressed challenges stemming from uncertainties about a bot's security, reliability, and fit through emergent social ecosystems. Formal and informal opportunities to discuss bots with others across communities enabled participants to revise their technological frames over time, closing gaps in bot-specific skills and knowledge. This social process of learning shifted participants' perspectives of the labor of bot adoption into something that was satisfying and fun, underscoring the value of collaborative and communal approaches to adopting bots. Finally, by shaping participants' mental models of the nature, value, and use of bots, social ecosystems also raise some practical tensions in how they support user creativity and customization in third-party bot use. Together, the social nature of adopting third-party bots in our interviews offers insight into how we can better support the sharing of valuable user-facing tools across online communities.

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.

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.

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.

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.

Why do people start new online communities? Previous research has studied what helps communities to grow and what motivates contributors, but the reasons that people create new communities in the first place remain unclear. We present the results of a survey of over 300 founders of new communities on the online wiki hosting site Wikia.com. We analyze the motivations and goals of wiki creators, finding that founders have diverse reasons for starting wikis and diverse ways of defining their success. Many founders see their communities as occupying narrow topics, and neither seek nor expect a large group of contributors. We also find that founders with differing goals approach community building differently. We argue that community platform designers can create interfaces that support the diverse goals of founders more effectively.

Trust is essential for beginning and maintaining relationships online where assessing uncertainties and risks is difficult. While product delays have been shown to reduce trust in e-commerce settings, we understand little about the effect of delays on trust in the increasingly popular context of crowdfunding. In a mixed method study, we examine what factors influence backers' trust in crowdfunding when rewards are delayed. Based on in-depth interviews with crowdfunding participants, we found that a rich set of factors influenced backers' trust including backers' role identity and domain knowledge, backer's research on a creator's background, creators' communication during delays, and duration of delays. To better understand the factors affecting delays, we conducted a regression analysis with 4,089 delayed projects and found that the funding goal, number of backers, percent raised, number of reward levels, and creator's previous crowdfunding experience are associated with the duration of delay. We discuss design implications for managing delays and maintaining trust in crowdfunding.

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.

Social movements use social computing systems to complement offline mobilizations, but prior literature has focused almost exclusively on movement actors' use of social media. In this paper, we analyze participation and attention to topics connected with the Black Lives Matter movement in the English language version of Wikipedia between 2014 and 2016. Our results point to the use of Wikipedia to (1) intensively document and connect historical and contemporary events, (2) collaboratively migrate activity to support coverage of new events, and (3) dynamically re-appraise pre-existing knowledge in the aftermath of new events. These findings reveal patterns of behavior that complement theories of collective memory and collective action and help explain how social computing systems can encode and retrieve knowledge about social movements as they unfold.

Recent years have seen the growth of physical crowdsourcing systems (e.g., Uber; TaskRabbit) that motivate large numbers of people to provide new and improved physical tasking and delivery services on-demand. In these systems, opportunistically relying on people to make convenient contributions may lead to incomplete solutions, while directing people to do inconvenient tasks requires high incentives. To increase people's willingness to participate and reduce the need to incentivize participation, we study on-the-go crowdsourcing as an alternative approach that suggests tasks along people’s existing routes that are conveniently on their way. We explore as a first step in this paper the design of task notification policies that decide when, where, and to whom to suggest tasks. Situating our work in the context of practical problems such as package delivery and lost-and-found searches, we conducted controlled experiments that show how small changes in task notification policy can influence individual participation and actions in significant ways that in turn affect system outcomes. We discuss the implications of our findings on the design of future on-the-go crowdsourcing technologies and applications.

In emergency response organizations like the fire service, personnel require easy access to reliable, up-to-date safety protocols. Systems for creating and managing Standard Operating Procedures (SOPs) within these command and control organizations are often rigid, inaccessible, and siloed. Open collaboration systems like wikis and social computing tools have the potential to address these limitations, but have not been analyzed for intra-organizational use in emergency services. In response to a request from the Fire Protection Research Foundation (FPRF) we evaluated a high-fidelity open collaboration system prototype, FireCrowd, that was designed to manage SOPs within the U.S. fire service. We use the prototype as a technology probe and apply human-centered design methods in a suburban fire department in the Chicago area. We find that organizational factors would inhibit the adoption of some open collaboration practices and identify points in current practices that offer opportunities for open collaboration in the future.

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.

Paid crowd work offers remarkable opportunities for improving productivity, social mobility, and the global economy by engaging a geographically distributed workforce to complete complex tasks on demand and at scale. But it is also possible that crowd work will fail to achieve its potential, focusing on assembly-line piecework. Can we foresee a future crowd workplace in which we would want our children to participate? This paper frames the major challenges that stand in the way of this goal. Drawing on theory from organizational behavior and distributed computing, as well as direct feedback from workers, we outline a framework that will enable crowd work that is complex, collaborative, and sustainable. The framework lays out research challenges in twelve major areas: workflow, task assignment, hierarchy, real-time response, synchronous collaboration, quality control, crowds guiding AIs, AIs guiding crowds, platforms, job design, reputation, and motivation.

In this study we extend research on online collaboration by examining motivation to do work on the crowdsoucing service Amazon Mechanical Turk (MTurk). We address a challenge to many existing studies of motivation in online contexts: they are based on survey self-reports, which are susceptible to effects such as social desirability bias. In addition we investigate a second challenge to the extant research on motivation in the context of MTurk: a failure to examine potential differences between MTurk workers (Turkers) from different parts of the world, especially those from the US and India, MTurk's two largest worker groups. Using a survey technique called the list experiment, we observe distinct profiles of motivation and patterns of social desirability effects among Turkers in the US and India. Among US Turkers, we find that social desirability encourages over-reporting of each of four motivating factors we examined. The over-reporting was particularly large in the case of money as a motivator. In contrast, among Turkers in India we find a more complex pattern of social desirability effects, with workers under-reporting "killing time" and "fun" as motivations, and drastically over-reporting "sense of purpose." We conclude by discussing these results and proposing implications for future research and design.

The emergence of online labor markets makes it far easier to use individual human raters to evaluate materials for data collection and analysis in the social sciences. In this paper, we report the results of an experiment - conducted in an online labor market - that measured the effectiveness of a collection of social and financial incentive schemes for motivating workers to conduct a qualitative, content analysis task. Overall, workers performed better than chance, but results varied considerably depending on task difficulty. We find that treatment conditions which asked workers to prospectively think about the responses of their peers - when combined with financial incentives - produced more accurate performance. Other treatments generally had weak effects on quality. Workers in India performed significantly worse than US workers, regardless of treatment group.