Forwarding this invite to additional people who may wish to participate or watch the recording.
Thanks, Kinneret. These presentations sound very interesting.
Pine🌲
---------- Forwarded message --------- From: Kinneret Gordon via Analytics analytics@lists.wikimedia.org Date: Mon, Feb 23, 2026 at 5:23 AM Subject: [Analytics] [Wikimedia Research Showcase] AI and Communities - February 25 at 17:30 UTC To: wiki-research-l@lists.wikimedia.org, < foundation-optional@wikimedia.org>, wikimedia-l@lists.wikimedia.org, < analytics@lists.wikimedia.org> Cc: Kinneret Gordon kgordon@wikimedia.org
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
#%~~<|~\£~££
On Wed, 25 Feb 2026 at 11:49 AM Pine W via AI ai@lists.wikimedia.org wrote:
Forwarding this invite to additional people who may wish to participate or watch the recording.
Thanks, Kinneret. These presentations sound very interesting.
Pine🌲
---------- Forwarded message --------- From: Kinneret Gordon via Analytics analytics@lists.wikimedia.org Date: Mon, Feb 23, 2026 at 5:23 AM Subject: [Analytics] [Wikimedia Research Showcase] AI and Communities - February 25 at 17:30 UTC To: wiki-research-l@lists.wikimedia.org, < foundation-optional@wikimedia.org>, wikimedia-l@lists.wikimedia.org, < analytics@lists.wikimedia.org> Cc: Kinneret Gordon kgordon@wikimedia.org
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/* _______________________________________________ Analytics mailing list -- analytics@lists.wikimedia.org To unsubscribe send an email to analytics-leave@lists.wikimedia.org _______________________________________________ AI mailing list -- ai@lists.wikimedia.org To unsubscribe send an email to ai-leave@lists.wikimedia.org