Online communities and their host platforms are mutually dependent yet conflict-prone. When platform policies clash with community values, communities have resisted through strikes, blackouts, and even migration to other platforms. Through such collective actions, communities have sometimes won concessions, but these have frequently proved to be temporary. Although previous research has investigated strike events and migration chains, the processes by which community-platform conflict unfolds remain obscure. How do community-platform relationships deteriorate? How do communities organize collective action? How do the participants proceed in the aftermath? We investigate a conflict between the Stack Exchange platform and community that occurred in 2023 around an emergency arising from the release of large language models (LLMs). Based on a qualitative thematic analysis of 2,070 messages from Meta Stack Exchange and 14 interviews with community members, we reveal how the 2023 conflict was preceded by a long-term deterioration in the community-platform relationship, driven in particular by the platform’s disregard for the community’s highly valued participatory role in governance. Moreover, the platform’s policy response to LLMs aggravated the community’s sense of crisis, triggering strike mobilization. We analyze how the mobilization was coordinated through a tiered leadership and communication structure, as well as how community members pivoted in the aftermath. Building on recent theoretical scholarship in social computing, we use Hirschman’s exit, voice, and loyalty framework to theorize the challenges of community-platform relations evinced in our data. Finally, we recommend ways that platforms and communities can institute participatory governance to be durable and effective.

Online communities are important organizational forms where members socialize and share information. Curiously, different online communities often overlap considerably in topic and membership. Recent research has investigated competition and mutualism among overlapping online communities through the lens of organizational ecology; however, it has not accounted for how the nonlinear dynamics of online attention may lead to episodic competition and mutualism. Neither has it explored the origins of competition and mutualism in the processes by which online communities select or adapt to their niches. This paper presents a large-scale study of 8,806 Reddit communities belonging to 1,919 clusters of high user overlap over a 5-year period. The method uses nonlinear time series methods to infer bursty, often short-lived ecological dynamics. Results reveal that mutualism episodes are longer lived and slightly more frequent than competition episodes. Next, it tests whether online communities find their niches by specializing to avoid competition using panel regression models. It finds that competitive ecological interactions lead to decreasing topic and user overlaps; however, changes that decrease such niche overlaps do not lead to mutualism. The discussion considers future designs for online community ecosystem management.

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.

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.

Organizing complex peer production projects and advancing scientific knowledge of open collaboration each depend on the ability to measure quality. Wikipedia community members and academic researchers have used article quality ratings for purposes like tracking knowledge gaps and studying how political polarization shapes collaboration. Even so, measuring quality presents many methodological challenges. The most widely used systems use quality assesements on discrete ordinal scales, but such labels can be inconvenient for statistics and machine learning. Prior work handles this by assuming that different levels of quality are “evenly spaced” from one another. This assumption runs counter to intuitions about degrees of effort needed to raise Wikipedia articles to different quality levels. I describe a technique extending the Wikimedia Foundations’ ORES article quality model to address these limitations. My method uses weighted ordinal regression models to construct one-dimensional continuous measures of quality. While scores from my technique and from prior approaches are correlated, my approach improves accuracy for research datasets and provides evidence that the “evenly spaced” assumption is unfounded in practice on English Wikipedia. I conclude with recommendations for using quality scores in future research and include the full code, data, and models.

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.

Much existing knowledge about global consumption of peer-produced information goods is supported by data on Wikipedia page view counts and surveys. In 2017, the Wikimedia Foundation began measuring the time readers spend on a given page view (dwell time), enabling a more detailed understanding of such reading patterns. In this paper, we validate and model this new data source and, building on existing findings, use regression analysis to test hypotheses about how patterns in reading time vary between global contexts. Consistent with prior findings from self-report data, our complementary analysis of behavioral data provides evidence that Global South readers are more likely to use Wikipedia to gain in-depth understanding of a topic. We find that Global South readers spend more time per page view and that this difference is amplified on desktop devices, which are thought to be better suited for in-depth information seeking tasks.

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.