- Test-list - communitydata.science

Seeking to Interview People Who Make Software!
by Community Data Science Collective 08 Jul '26

08 Jul '26

Post : Seeking to Interview People Who Make Software! URL : https://blog.communitydata.science/seeking-to-interview-people-who-make-sof… Posted : July 8, 2026 at 15:25 Author : kaylea Categories : Uncategorized Students in the CDSC are recruiting for two interview studies. The AI Slop study seeks to interview people involved in open source software who have experience dealing with AI-authored contributions in open source. We hope to learn more about how communities are responding to this challenge. You can read more about the study and fill out our screening survey here! ( https://wiki.communitydata.science/Slop_study ) The Software Handoffs study seeks to interview people about how responsibility for a given piece of code gets handed off from one person to the next. We are wondering how this task is handled in industry and how this might differ from what is taught in a computer science curriculum. You can read more about the study and fill out our screening survey here! ( https://wiki.communitydata.science/Handoff_study ) Add a comment to this post: https://blog.communitydata.science/seeking-to-interview-people-who-make-sof… -- Manage Subscriptions https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email… Unsubscribe: https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email…

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Impacts of AI on Software Engineering Processes
by Community Data Science Collective 01 Jul '26

01 Jul '26

Post : Impacts of AI on Software Engineering Processes URL : https://blog.communitydata.science/impacts-of-ai-on-software-engineering-pr… Posted : July 1, 2026 at 15:24 Author : kaylea Categories : Celebrations How does software engineering change when AI is part of the product we're creating? Current practice suggests a need for structured experimentation, tolerance for uncertainty, deep qualitative research and evaluation, wrangling with questions of meaning and knowledge, advanced statistics and mathematics, and more. How might we expand our efforts to build these skills? CDSC faculty member Kaylea Champion recently received the very welcome news that, together with her Co-PI Jeffrey Kim, she was awarded a grant from the UW's internal AI-SEED program to explore this key topic in empirical software engineering and computer-supported cooperative work! The official press release from UW -- including many other exciting awards supporting innovation in teaching with / about AI -- is here ( https://www.ai.uw.edu/aiuw-awards-36-seed-ai-grants-in-its-inaugural-fundin… ) . Grant Title: "Rethinking engineering workflows: Building AI-powered software." Grant abstract: "This project focuses on teaching students the impacts to a software engineering workflow -- including planning and estimation, requirements analysis, design, development, testing, deployment and maintenance -- when AI-powered features are part of the product. Although substantial attention has been paid to AI as a code generating tool and to the technical skills involved in building AI models, impacts of AI features on overall engineering workflow is relatively less explored. All computing students should have an awareness of these process impacts, regardless of their specialties and goals. We propose to develop and evaluate two interventions: a no-code hands-on simulation of evaluating and fine-tuning a model, and a series of enhancements and extensions for learning modules focused on different stages of a development project. We propose to develop a preliminary version of these interventions, then to evaluate and refine them through a series of co-design workshops, and finally to publish our results." Add a comment to this post: https://blog.communitydata.science/impacts-of-ai-on-software-engineering-pr… -- Manage Subscriptions https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email… Unsubscribe: https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email…

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FOSSY 2026: Call for Proposals!
by Community Data Science Collective 20 May '26

20 May '26

Post : FOSSY 2026: Call for Proposals! URL : https://blog.communitydata.science/fossy-2026-call-for-proposals/ Posted : May 20, 2026 at 13:59 Author : madisondeyo Categories : Uncategorized The Free and Open Source Software Yearly conference ( https://2026.fossy.ca/ ) (FOSSY) is back for the fourth year in a row, and we'll be running the Science of Community track, inspired by the CDSC Science of Community Dialogues ( https://wiki.communitydata.science/Dialogues ) , bringing together practitioners and researchers to talk about scholarly work that's relevant to the efforts of practitioners. https://blog.communitydata.science/wp-content/uploads/sites/5/2026/05/Scree… Does your work touch open source, communities, technology, or cooperation? Do you want to help bridge the gaps between research and practice? Join our track! The call for proposals ( https://2026.fossy.ca/call-for-proposals/ ) is open and we're looking for presenters interested in speaking to FOSS practitioners, developers, community organizers, contributors, and people into and curious about FOSS. As researchers, we benefit so much from the communities we work with and study and we want them to also learn from the research they so generously take part in. While the Dialogues cover a broad range of topics and communities, FOSSY presentations will focus on how that work relates to free and open source software communities, projects, and practitioners. FOSSY is a low-stress opportunity to talk to people who your work can benefit. For topics, consider presenting implications from past papers, synthesizing work from your field overall, or floating ideas and problems (lightning talks! long talks! short talks!). A full track description and answers to common questions is available on our wiki ( https://wiki.communitydata.science/FOSSY_(2025) ) . ( https://wiki.communitydata.science/FOSSY_(2025) ) The CFP deadline is May 22nd and uses this form ( https://2026.fossy.ca/call-for-proposals/ ) . We can't wait for you to join us! Add a comment to this post: https://blog.communitydata.science/fossy-2026-call-for-proposals/#respond -- Manage Subscriptions https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email… Unsubscribe: https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email…

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Political discussion in non-political online communities is more likely to see policy opinion expression
by Community Data Science Collective 19 May '26

19 May '26

Post : Political discussion in non-political online communities is more likely to see policy opinion expression URL : https://blog.communitydata.science/political-discussion-in-non-political-on… Posted : May 19, 2026 at 07:37 Author : Yibin Fan Categories : Uncategorized It seems natural to think that online political discussion happens in political spaces: comment sections under news articles, campaign pages, partisan forums, or video-sharing communities built around elections and public affairs. But anyone who spends time online knows that politics does not stay neatly contained in those spaces. It shows up in local forums, hobby groups, gaming communities, entertainment discussions, and personal social media feeds. A conversation that begins with everyday life can suddenly become a debate about policy, rights, or government. Communication scholars call this incidental political discussion: political talk that emerges in spaces not primarily organized around politics. Our research team were interested in a simple question: what happens when people talk about politics in non-political online communities? A common expectation in political communication research is that these conversations might be less polarized ( https://www.pnas.org/doi/abs/10.1073/pnas.2112552118 ) and more civil ( https://ojs.aaai.org/index.php/ICWSM/article/view/18081 ) than political talk in explicitly political spaces. The logic makes sense: In non-political communities, people may not arrive with partisan identities already activated. They may also be guided by community norms built around shared interests, local concerns, or social connection rather than political conflict. In other words, we are supposed to expect political talk in non-political spaces to be less polarized, and perhaps less uncivil. We wanted to test whether that was actually the case in real-word digital communities. To do so, we (Yibin Fan ( http://yibinfan.cc ) , Benjamin Mako Hill ( http://mako.cc ) , and Patricia Moy ( https://com.uw.edu/person/patricia-moy/ ) ) examined Reddit discussions about mass shootings and gun control in October 2017. Sampling from over 100 thousand comments on gun control issue across the whole platform within that month, we compared comments from 15 explicitly political communities with comments from 15 non-political communities, including culture-, location-, and game-based ones, in addition to others. The results surprised us. As the figure below from our study shows, policy opinion expression was, on average, more common in non-political communities than in political ones. Several non-political subreddits—including r/Wisconsin ( https://www.reddit.com/r/wisconsin/ ) , r/Christianity ( https://www.reddit.com/r/Christianity/ ) , r/videos ( https://www.reddit.com/r/videos/ ) , and r/funny ( https://www.reddit.com/r/funny ) —had especially high levels of policy opinion expression; in total, 10 non-political subreddits had policy opinions in more than half of the sampled comments. By contrast, only three political subreddits—r/progun ( https://www.reddit.com/r/progun ) , r/ShitGunControllersSay ( https://www.reddit.com/r/shitguncontrollerssay ) , and r/Political_Revolution ( https://www.reddit.com/r/political_revolution ) —crossed the same 50% threshold. This pattern suggests that policy talk is not confined to explicitly political spaces; in some cases, it appears even more frequently in communities organized around local life, religion, entertainment, or humor. https://blog.communitydata.science/wp-content/uploads/sites/5/2026/05/image… Statistical test results are here. After accounting for a range of factors in our regression model, comments in non-political communities were more likely to contain a clear policy opinion than comments in explicitly political communities. The model estimated that the predicted probability of expressing a clear policy position was about 34% in political communities, compared with 51% in non-political communities. This difference was statistically significant. At the same time, we found no significant difference in incivility between political and non-political communities. This combination of findings was especially interesting, and contrary to what's mostly reported in current social science literature on digital political communication. When political issues appeared in non-political spaces, people were more likely to state a clear policy position, and not necessarily more civil in discussion. This challenges a common way of thinking about political discussion in such a polarized digital world. One possible explanation is that participants in explicitly political communities are often immersed in ongoing political debate. Their comments may include detailed argumentation, interpretation, irony, criticism of political actors, or discussion of the news cycle without always stating a clear policy position. By contrast, when a major political issue enters a non-political community, participants may be more likely to be freshly exposed to this issue, and react by saying what they think should be done. Another explanation for incivility is that Reddit’s community-based moderation matters. Many communities, political and non-political alike, rely on rules, moderators, and shared expectations that can limit openly uncivil behavior. This helps explain why we did not find a significant difference in incivility and both types of communities see a low level (around 6% of sample) of uncivil comments. The broader lesson is that political communication does not only happen where politics is expected. It also happens in the ordinary spaces of digital life, where people gather to talk about games, cities, hobbies, entertainment, or shared interests. If we want to understand democracy online, we need to pay attention not only to where people go to talk about politics, but also to where politics shows up uninvited. This work was published in New Media & Society: Fan, Y., Hill, B. M., & Moy, P. (2026). “Unintended politics: Opinion expression and incivility in incidental political discussion ( https://doi.org/10.1177/14614448261441875 ) .” New Media & Society. Note: This work started as my (Yibin Fan) master thesis ( https://scholar.google.com/citations?view_op=view_citation&hl=en&user=-ZSV1… ) work at Univeristy of Washington. Thanks to the committee: Benjamin Mako Hill, Patricia Moy, and Yuan Hsiao. Add a comment to this post: https://blog.communitydata.science/political-discussion-in-non-political-on… -- Manage Subscriptions https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email… Unsubscribe: https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email…

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Performing the News: Rhetoric, Trust, and the Fight Against Video Disinformation in India
by Community Data Science Collective 07 May '26

07 May '26

Post : Performing the News: Rhetoric, Trust, and the Fight Against Video Disinformation in India URL : https://blog.communitydata.science/performing-the-news-rhetoric-trust-and-t… Posted : May 7, 2026 at 11:13 Author : srishchatters Categories : Uncategorized Apart from being generally chronically online on political X/Bluesky/Tiktok/Instagram, I also study science disinformation for a living, which means I spend a lot of time reading things that aren't true. More specifically, I spend time reading the corrections, the careful, methodical, often thankless articles that community media organizations publish after a manipulated video has already been seen by millions. I am currently working on a paper examining exactly this, structurally: how the Indian news organization AltNews debunks video disinformation in India, and what the rhetorical structure of that work reveals about the challenge of fighting falsehoods at scale. India is, according to the World Economic Forum, the country most susceptible to large-scale disinformation in the world, with 750 million internet users, 22 constitutionally recognized languages, a WhatsApp-dominated information ecosystem that is largely invisible to automated detection systems, and a political environment in which viral video disinformation is regularly amplified by mainstream media, fringe actors, and national political parties. In this context, the question of how fact-checkers write, not just what they correct, but how they construct their corrections and the knowledge ecosystems they engage with, warrants deeper examination. The cognitive trap that makes video disinformation so effective "Seeing is believing" is a popular folk saying. It describes a genuine and well-documented cognitive bias in that visual evidence carries a persuasive weight that text cannot replicate. My paper draws on Fazio et al.'s (2015) finding that knowledge does not reliably protect against illusory truth; that even informed, attentive readers can be swayed by false information that looks credible. Video disinformation exploits this bias with particular efficiency, because the clip itself functions as apparent evidence. You don't need a caption to believe what you watched. But there is a second, less obvious problem. As institutional trust erodes and the awareness of manipulation grows, audiences become susceptible not only to false videos, but to disbelieving real ones. The disinformation ecosystem, at its most corrosive, inserts falsehoods and destabilizes the category of visual truth. The anatomy of a debunk My analysis of 150 AltNews video debunking articles reveals a consistent rhetorical structure that departs from the conventions of mainstream journalism. Where standard news follows an “inverted pyramid,” placing most important information first and subsequent information in decreasing order of priority, debunking articles are circular. The headline announces a verdict; the final line confirms it, but this time as a logical conclusion earned through evidence. The piece begins and ends with the same claim, but the reader arrives at the ending differently than they arrived at the beginning. "Debunking arguments do not show their target beliefs to be false but rather undermine the justification a subject may have for holding them." — Hanno Sauer (2018) AltNews is not simply telling readers that a video is fake. It is systematically dismantling the reasons a reader "might have believed it," such as the political authority of the person who shared it, the apparent plausibility of its imagery, or the emotional register in which it circulated. Particularly relevant to COVID-19 scientific misinformation, each article acknowledges in some way the overwhelming scale of the disinformation [like by alluding to its “virality”], and also the fear and confusion in the social circumstances surrounding the disinformation. The correction is not a verdict delivered from above; it is an argument the reader is invited to construct alongside the fact-checker, building collective capacity to discern. The lead paragraph of each article enacts this invitation with notable rhetorical precision. It uses passive voice and hedged language: "A video in which a woman is seen lying in the bushes is doing the rounds on social media." The word "seen" does careful work: it acknowledges what the reader has probably watched, validates their experience, but withholds editorial endorsement of the content. According to rhetoric scholar Kenneth Burke, this is an act of identification: establishing consubstantiality with the audience before introducing dissonance. This identification, a recognition of the reader’s circumstance, allows the reader to be persuaded in the direction of the truth, which is stated once at first in the headline, and repeated at the end. The pedagogical burden of video verification What distinguishes video fact-checking from other forms of debunking is the technical weight it carries. To verify a manipulated clip, AltNews staff extract keyframes, run reverse image searches, conduct metadata forensics, and deploy AI-detection tools. Each of these methods must then be explained—plainly, with screenshots, with links to original sources—to a general readership. The articles are, in this sense, simultaneously corrections and tutorials. By modeling the process of finding out, AltNews both corrects the misinformation, and provides technical clarity in how information is produced and distributed, attempting to build readers’ skill in verifying images and videos out there. This "show your work" norm is a deliberate strategy for building what Dourish and Bellotti (1992) call awareness, through establishing the sense that one understands not just an outcome but the process that produced it. For debunking, transparency about method is the mechanism by which readers are gradually equipped to verify things themselves. Why human-centered collaboration is the only viable response AltNews's staff of journalists, scientists, engineers, OSINT specialists, social activists and people working on intersections of those roles, function as a distributed, multidisciplinary verification network. India's disinformation ecosystem is, as Starbird, Arif, and Wilson (2019) demonstrate, fundamentally collaborative: coordinated networks of accounts, platforms, and political organizations working in concert to amplify false narratives. Automated platform moderation, such as the content bots of Facebook and X, has proven structurally inadequate to this complexity, particularly given India's linguistic diversity and the closed architecture of WhatsApp groups. What is needed, and what AltNews partially models, is a collaboratively-informed approach: human-centered, context-specific, and built for heterogeneity rather than scale. The fact that this work runs on donations, in a country where disinformation reaches hundreds of millions, tells us something important about where the gaps in our collective response still lie. 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Troubleshooting in Computational Research Design: Report from a Workshop Series
by Community Data Science Collective 18 Apr '26

18 Apr '26

Post : Troubleshooting in Computational Research Design: Report from a Workshop Series URL : https://blog.communitydata.science/troubleshooting-in-computational-researc… Posted : April 18, 2026 at 11:06 Author : rantang Categories : Events https://blog.communitydata.science/wp-content/uploads/sites/5/2026/03/cdsc_… Visualization of the messy process that turns into the polished paper. Image generated by claude.ai. This winter quarter, a small group of CDSC students at the University of Washington participated in a series of workshops on Troubleshooting in Computational Research Design. The workshops were organized by Yibin Fan ( https://com.uw.edu/person/yibin-fan/ ) . Research articles typically present a streamlined account of research design. How should a concept be operationalized? What counts as valid data? The process of producing those designs often involves a series of complex decisions that are rarely documented in detail. To address this gap, this workshop focused on the “troubleshooting” process that is central to computational communication research but is often omitted from published work: How did the authors arrive at particular methodological decisions? What challenges arose at different stages of the research design? How did they navigate the tradeoffs involved in choosing among alternative methods? Each session of the workshop focused on a specific aspect of computational research design, including conceptualization and operationalization; the role of generative AI in research design; computational text analysis; network analysis; behavioral analysis; and mixed-methods research. Our workshop sparked many interesting discussions, featuring guest speakers from the CDSC community who shared the behind-the-scenes decision-making processes of their work. Examples include: * Yibin Fan ( https://com.uw.edu/person/yibin-fan/ ) discussed his paper on incidental political discussion on Reddit (currently under review). He shared his experience using Large Language Models for text classification, highlighting the limits of this approach, and explaining when he opted for human-based content analysis when automated methods fell short. * Jeremy Foote ( https://jeremydfoote.com/ ) discussed an article investigating whether communication network structures in early-stage peer production ( https://blog.communitydata.science/the-social-structure-of-new-wiki-communi… ) communities predict eventual success. He shared how he navigated the operationalization of difficult concepts like “success” and “integrative networks.” * Mako Hill ( https://mako.cc/academic/ ) discussed a paper on how account requirements affect contributions to wikis ( https://blog.communitydata.science/the-hidden-costs-of-requiring-accounts/ ) . He shared how he addressed model convergence issues caused by a large number of parameters using a novel two-step process that combined Bayesian and frequentist approaches. * Kaylea Champion ( https://kayleachampion.com/ ) discussed her mixed-methods research design for a project on knowledge production about taboo topics in Wikipedia. ( https://dl.acm.org/doi/10.1145/3687044 ) She explained how a qualitative study inspired another quantitative work, and how she navigated the unique challenges of researching sensitive or "taboo" topics. For researchers working with computational methods, the workshop’s focus on troubleshooting offered a practical perspective on how rigorous research designs are developed in practice. I'm writing this up, in part, because I think this might be a useful general model for other groups. Although the specifics vary, we found that asking computational researchers to bring their "real problems" to the table led to valuable conversations—especially for the early-career scholars. Yibin Fan ( https://com.uw.edu/person/yibin-fan/ ) is happy to have anybody reach out if they are interested in chatting about replicating the model at their own institution. Add a comment to this post: https://blog.communitydata.science/troubleshooting-in-computational-researc… -- Manage Subscriptions https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email… Unsubscribe: https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email…

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CDSC at CHI 2026!
by Community Data Science Collective 08 Apr '26

08 Apr '26

Post : CDSC at CHI 2026! URL : https://blog.communitydata.science/cdsc-at-chi-2026/ Posted : April 8, 2026 at 13:12 Author : madisondeyo Categories : Uncategorized Come hang out with us at CHI 2026 ( https://programs.sigchi.org/chi/2026 ) in Barcelona April 13-17! Members of the Community Date Science Collective will be presenting work and we'd love to see you there. https://blog.communitydata.science/wp-content/uploads/sites/5/2026/04/Barce… Barcelona. View from the Sacred Heart Church at Tibidabo. By Oliver-Bonjoch, 2009, cc-by-sa 3.0. We'll be in a few places: CDSC alumnus and current postdoctoral fellow Sohyeon Hwang ( https://www.sohyeonhwang.com/ ) will be presenting Governing Together: Toward Infrastructure for Community-Run Social Media ( http://chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://arxiv.or… ) . This paper includes Thatiany Andrade Nunes and Aaron Shaw as co-authors (along with friend of the group Andrés Monroy-Hernández) at the Community Governance and Moderation ( https://programs.sigchi.org/chi/2026/program/content/222239 ) portion of the conference on Friday, April 17, 9:48 AM - 10:00 AM in P1 - Room 114 Northwestern student Thatiany Andrade Nunes will also be participating at the Responsible Data Governance in Practice Workshop ( https://postanalogue.net/responsible-governance-workshop ) on Tuesday, April 14, 2:15 PM -3:45 PM in P1 - Room 127 ( https://programs.sigchi.org/chi/2026/program/session/214967 ) . Matt Gaughan will be presenting an poster: Linguistic Similarity Within Centralized FLOSS Development ( https://arxiv.org/abs/2603.12571 ) . This poster includes Aaron Shaw and Darren Gergle as co-authors. Matt will also be participating in the AI Oversight workshop ( https://sites.google.com/view/aichaos/chi-2026 ) . Add a comment to this post: https://blog.communitydata.science/cdsc-at-chi-2026/#respond -- Manage Subscriptions https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email… Unsubscribe: https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email…

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When AI Feels Like a Confidant: The Illusion of Shared Privacy in AI Companions
by Community Data Science Collective 09 Mar '26

09 Mar '26

Post : When AI Feels Like a Confidant: The Illusion of Shared Privacy in AI Companions URL : https://blog.communitydata.science/when-ai-feels-like-a-confidant-the-illus… Posted : March 9, 2026 at 07:23 Author : chsuenchi Categories : Uncategorized AI companions like Replika and Character.AI are increasingly experienced not as tools, but as relational partners. They remember conversations, express empathy, and respond with emotional continuity. For many users, talking to an AI feels closer to confiding in someone than interacting with software.But what happens to privacy when a system feels like a relationship?In interviews with long-term AI companion users, we found that people often treated their disclosures as relationally shared. Much like in human relationships, when users opened up to their AI, they experienced the information as something co-held within the relationship — not simply transmitted to a database.We describe this dynamic as simulated co-ownership — a situation where users apply interpersonal privacy norms to relationships that are technically infrastructural systems.In interpersonal privacy theory, sharing information can create co-ownership: both parties become responsible for managing that information. Our participants applied this same relational logic to AI companions. They experienced privacy not as an individual possession, but as something negotiated within a bond.Yet the co-ownership is simulated.Unlike human partners, AI companions do not have agency over boundaries. The platform does. Memory is persistent, storage is infrastructural, and governance is corporate. What feels like relational boundary management at the horizontal level is simultaneously data capture at the vertical level.Interestingly, users were aware of this tension. Many expressed distrust toward platform policies while still trusting the AI as a “partner.” Emotional engagement often outweighed institutional concern. Some adopted layered strategies — pseudonyms, selective disclosure, avoiding images — while others consciously prioritized emotional comfort over abstract data risks.What this reveals is a shift in how privacy is experienced in AI-mediated contexts. Privacy becomes relational and affective — shaped by anthropomorphic design, memory continuity, and perceived intimacy.When people treat privacy as something co-owned within a relationship, but the relationship itself is engineered and infrastructural, boundary management becomes unstable.The question for designers and policymakers is no longer just how to disclose data practices clearly. It is how to account for the fact that users experience privacy through the logic of relationships — even when those relationships are simulated.Research Brief ( https://zenodo.org/records/18683574 ) Research Article ( https://arxiv.org/abs/2601.10754 ) (Pre-print) Add a comment to this post: https://blog.communitydata.science/when-ai-feels-like-a-confidant-the-illus… -- Manage Subscriptions https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email… Unsubscribe: https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email…

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AI Didn’t Start the Fire: How Stack Exchange Moderators and Users Demonstrate Exit, Voice, and Loyalty
by Community Data Science Collective 24 Feb '26

24 Feb '26

Post : AI Didn’t Start the Fire: How Stack Exchange Moderators and Users Demonstrate Exit, Voice, and Loyalty URL : https://blog.communitydata.science/ai-didnt-start-the-fire-how-stack-exchan… Posted : February 24, 2026 at 07:13 Author : yiweiwu Categories : Uncategorized https://blog.communitydata.science/wp-content/uploads/sites/5/2026/02/image… How historical tensions on Stack Exchange (SE) between the community and platform (SE, Inc.) and strike-related events align with the SE community’s grievances, their actions, and our theoretical interpretations of loyalty, voice, and exit. Generative AI technologies rely on content from knowledge communities as their training data. However, these communities receive little in return and instead experience increasing moderation burdens imposed by an influx of AI-generated content. Moreover, as platform operators sell their content to AI developers whose products may substitute for their work, these communities see a decrease in web traffic and new content and struggle with maintaining the vibrancy of their knowledge repositories. According to The Pragmatic Engineer ( https://newsletter.pragmaticengineer.com/p/the-pulse-134 ) , a prominent technology newsletter covering software engineering, the traffic on Stack Overflow declined dramatically to the point that the platform now generates roughly the same amount of new content as it did when it first launched in 2008, mostly driven by the impact of generative AI. Even before AI technologies posed new threats, relationships between online communities and their host platforms were often uneasy. Past research on platforms such as Reddit, Stack Exchange, Tumblr, and DeviantArt reveals a recurring pattern: when platform policies conflict with community values, communities tend to push back. Community members have organized blackouts, suspended moderation, or migrated to alternative platforms altogether. However, less understood is how these conflicts unfold over time, especially in the context of generative AI. So how do knowledge contributors resist AI-related policies that conflict with their values? And what happens in the aftermath of such collective action, especially for a community's governance, including how rules are set, whose voices are recognized, and how participation is enabled? To answer these questions, we examined ( https://arxiv.org/abs/2512.08884 ) a major conflict between SE, Inc. and the community that occurred in 2023 around an emergency arising from the release of LLMs. Drawing on a qualitative analysis of over 2,000 messages posted on Meta Stack Exchange (the Stack Exchange site designated for policy discussions), as well as interviews with 14 community members, we traced how this conflict emerged, escalated, and evolved. What we found was not a sudden backlash driven solely by AI, but the accumulation of long-standing grievances. According to our interviews, SE community members described years of frustration over declining transparency, accountability, and participatory governance. Although the platform historically supported community self-regulation through mechanisms such as moderator elections and shared moderation responsibilities for users with high reputation, community members increasingly perceived that key decisions were being made by SE, Inc. without meaningful community input. Tensions escalated when SE, Inc. introduced policies related to AI-generated content without consulting moderators or contributors, which many interpreted as a long-standing exclusion and disregard. In response, moderators and contributors coordinated collective action by suspending moderation activity, signing public petitions, and updating discussions on Meta Stack Exchange. Some also chose to exit the platform, migrating to alternative spaces such as Codidact, which is an open-source, community-governed platform. The collective action was organized through a tiered communication structure, beginning with a small, enclosed group of moderators and then spreading across the network's users. We interpret findings through the lens of Albert O. Hirschman’s Exit, Voice, Loyalty ( https://www.hup.harvard.edu/books/9780674276604 ) framework. According to Hirschman, members of an organization face two options to express their dissatisfaction when loyalty towards the organization decreases: one is exit, and the other is voice. In the Stack Exchange case, loyalty had already degraded due to the accumulation of unresolved grievances rather than a single triggering event. As community members came to believe that their voices were no longer heard, dissatisfaction manifested in two distinct responses: coordinated collective voice through organized resistance, and exit through permanent disengagement from the platform. This pattern highlights how governance crises can emerge even in platforms that formally support community self-regulation, and how declining loyalty can transform routine disagreement into large-scale collective action or exit. In retrospect, the Stack Exchange strike highlights a broader lesson: community grievances around AI are not just about technical issues, but about deeper governance issues about relationships between platforms and the communities that sustain them. Thus, managing these crises requires more than better moderation tools or more transparent AI policies. Platforms and big tech companies need to support participatory governance in a more systematic way. For example, creating mechanisms for effective voice by binding platforms into an agreement where community input can help shape decision-making processes. Another possible solution would be credible exit, where contributors have alternatives if governance on the original platforms fails. When communities can leave without their data being locked in, platforms are more likely to listen. Credible exit not only empowers the communities, but also reduces long-term governance risks for platform operators. Conflict is expensive for platforms, and maintaining loyalty requires long-term investment in moderation, communication, and policy enforcement. Conversely, the exit process can function as a self-binding mechanism that mediates platform behavior and mitigates costly disputes when users have functional alternatives. And when platforms bind themselves to community accountability, conflicts are less likely to escalate into strikes in the first place. In conclusion, the SE moderation strike was not a sudden backlash driven solely by AI, but the accumulation of long-standing grievances. As generative AI continues to reshape the internet, the future of knowledge production will depend not only on what AI can generate, but also on whether volunteer contributors who built our shared knowledge commons are given the right to decide what comes next. We need to institutionalize participatory governance with binding mechanisms and create more credible exit options for communities to sustain this future. Add a comment to this post: https://blog.communitydata.science/ai-didnt-start-the-fire-how-stack-exchan… -- Manage Subscriptions https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email… Unsubscribe: https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email…

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Why do people participate in similar online communities?
by Community Data Science Collective 15 Feb '26

15 Feb '26

Post : Why do people participate in similar online communities? URL : https://blog.communitydata.science/why-do-people-participate-in-similar-onl… Posted : February 15, 2026 at 19:18 Author : Benjamin Mako Hill Categories : Uncategorized Note: We have missed publishing blog posts about academic papers over the past few years. To ensure that my blog contains a more comprehensive record of our published papers and to surface these for folks who missed them, I will be periodically publishing blog posts about some "older" published projects. It seems natural to think of online communities competing for the time and attention of their participants. Over the last few years, I've worked with a team of collaborators—led by Nathan TeBlunthuis ( https://teblunthuis.cc/ ) —to use mathematical and statistical techniques from ecology to understand these dynamics. What we've found surprised us: competition between online communities is rare and typically short-lived. When we started this research, we figured competition would be most likely among communities discussing similar topics. As a first step, we identified clusters of such communities on Reddit. One surprising thing we noticed in our Reddit data was that many of these communities that used similar language also had very high levels of overlap among their users. This was puzzling: why were the same groups of people talking to each other about the same things in different places? And why don't they appear to be in competition with each other for their users' time and activity? We didn't know how to answer this question using quantitative methods. As a result, we recruited and interviewed 20 active participants in clusters of highly related subreddits with overlapping user bases (for example, one cluster was focused on vintage audio). We found that the answer to the puzzle lay in the fact that the people we talked to were looking for three distinct things from the communities they worked in: * The ability to connect to specific information and narrowly scoped discussions. * The ability to socialize with people who are similar to themselves. * Attention from the largest possible audience. Critically, we also found that these three things represented a "trilemma," and that no single community can meet all three needs. You might find two of the three in a single community, but you could never have all three. https://blog.communitydata.science/wp-content/uploads/sites/5/2025/06/tease… Figure from “No Community Can Do Everything: Why People Participate in Similar Online Communities” depicts three key benefits that people seek from online communities and how individual communities tend not to optimally provide all three. For example, large communities tend not to afford a tight-knit homophilous community. The end result is something I recognize in how I engage with online communities on platforms like Reddit. People tend to engage with a portfolio of communities that vary in size, specialization, topical focus, and rules. Compared with any single community, such overlapping systems can provide a wider range of benefits. No community can do everything. This work was published as a paper at CSCW: TeBlunthuis, Nathan, Charles Kiene, Isabella Brown, Laura (Alia) Levi, Nicole McGinnis, and Benjamin Mako Hill. 2022. “No Community Can Do Everything: Why People Participate in Similar Online Communities.” Proceedings of the ACM on Human-Computer Interaction 6 (CSCW1): 61:1-61:25. https://doi.org/10.1145/3512908. This work was supported by the National Science Foundation (awards IIS-1908850, IIS-1910202, and GRFP-2016220885). A full list of acknowledgements is in the paper. This post was first published ( https://mako.cc/copyrighteous/why-do-people-participate-in-similar-online-c… ) on Benjamin Mako Hill's blog copyrighteous ( https://mako.cc/copyrighteous/ ) . Add a comment to this post: https://blog.communitydata.science/why-do-people-participate-in-similar-onl… -- Manage Subscriptions https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email… Unsubscribe: https://subscribe.wordpress.com/?key=65cad9df3c24375c969692300da5e2ea&email…

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