Hello all, Just a quick reminder that our showcase on *AI and Communities *will begin in just over 30 minutes. You can watch it at* https://www.youtube.com/live/qW5IQJv84HY <https://www.youtube.com/live/qW5IQJv84HY>*. More information about the talks is included in the email below.
Best, Kinneret On Mon, Feb 23, 2026 at 3:23 PM Kinneret Gordon <[email protected]> wrote: > Hi everyone, > > The February 2026 Research Showcase will be live-streamed this Wednesday, > February 25, at 9:30 AM PT / 17:30 UTC. Find your local time here > <https://zonestamp.toolforge.org/1772040600>. Our theme this month is *AI > and Communities*. > > *We invite you to watch via the YouTube > stream: https://www.youtube.com/live/qW5IQJv84HY > <https://www.youtube.com/live/qW5IQJv84HY>.* As always, you can join the > conversation in the YouTube chat as soon as the showcase goes live. > > This month, we will have two presentations: > > *LLMs in Wikipedia: Investigating How LLMs Impact Participation in > Knowledge Communities*By *Moyan Zhou (University of Minnesota)*Large > language models (LLMs) are reshaping knowledge production as community > members increasingly incorporate them into their contribution workflows. > However, participating in knowledge communities involves more than just > contributing content - it is also a deeply social process shaped by > members' level of expertise. While communities must carefully consider > appropriate and responsible LLM integration, the absence of concrete norms > has left individual editors to experiment and navigate LLM use on their > own. Understanding how LLMs influence community participation across > expertise levels is therefore critical in shaping future norms and > supporting effective adoption. To address this gap, we investigated > Wikipedia, one of the largest knowledge production communities, to > understand participation in three dimensions: 1) how LLMs influence the > ways editors gather knowledge, 2) how editors leverage strategies to align > LLM outputs with community norms, and 3) how other editors in the community > respond to LLM-assisted contributions. Through interviews with 16 Wikipedia > editors with different levels of expertise who had used LLMs for their > edits, we revealed a participation gap mediated by expertise in adopting > LLMs in knowledge contributions across knowledge gathering, alignment with > community norms, and peer responses. Based on these findings, we challenge > existing models of novice editors' involvement and propose design > implications for LLMs that support community engagement, highlighting > opportunities for LLMs to sustain mentorship, knowledge transmission, and > legitimacy building by scaffolding and feedback, process documentation, and > LLM disclosure by good-faith editors.*AI Didn't Start the Fire: Examining > the Stack Exchange Moderator and Contributor Strike*By *Yiwei Wu > (University of Texas at Austin)*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. > Looking forward to seeing many of you, > Kinneret > -- > > Kinneret Gordon > > Lead Research Community Officer > > Wikimedia Foundation <https://wikimediafoundation.org/> > > *Learn more about Wikimedia Research <https://research.wikimedia.org/>* >
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