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Over twenty editions, the ICWSM community has examined social life online as platforms, interactions, and research methods have changed. What can this body of research tell us at this critical juncture, as AI increasingly reshapes how people communicate online? We analyzed 2,139 indexed contributions from 2007 to 2026,...
Online communities are valued not only for answers, but for the diversity of experiences and perspectives they contain. Generative search increasingly mediates access to this discourse, yet little is known about which community voices survive retrieval and synthesis. We audit Reddit Answers using 10,000 queries from 20...
A randomized vignette experiment with 285 U.S. adults across eight financial decisions independently varying three advice styles---AI, expert, and online community---and displayed source labels while holding the underlying recommendation consistent found expert-style advice remained most preferred when shown without so...
This work presents the first large-scale empirical comparison of AI-agent and human online communities, analyzing 73,899 Moltbook and 189,838 Reddit posts across five matched communities.
The findings suggest that open-source models, when trained with community-derived preference signals, can support high-quality mental health support assistance while offering a more privacy-preserving alternative for sensitive support contexts.
It is shown that this handoff step is a structural source of privacy leakage: summaries preferentially preserve operational facts while weakening the boundary metadata that governs how those facts may be used---a failure mode the authors call summary collapse.
It is suggested that local preference recognition requires community-specific training signal, not just better prompting, and this work supports future research on community-aware reward modeling, feed curation, and positive moderation.
It is found that LLMs outperform expectations at replicating comment structure and formality, but struggle to accurately capture nuanced emotions, e.g. understating joy and overstating anger.
Responses to real search queries are examined at three levels: source diversity, linguistic characterization of the generated summary, and source-summary fidelity, to find that generative search systems exhibit significant source-selection biases in their citations.
Findings show that financial-advice evaluations are shaped jointly by displayed attribution and message-level communication cues, which position disclosure not as a neutral transparency mechanism, but as an interpretive frame whose accuracy and interaction with message cues can shape trust and reliance.
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