arXiv AI
The Evolution of Coordination in a Collective Intelligence System: 25 Years of English Wikipedia and the Emergence of Generative AI
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arXiv AI
Jul 1
How Human Feedback Shapes AI-generated Community Notes
arXiv:2606. 30905v1 Announce Type: cross Abstract: Community Notes, a bridging-based crowd-sourced fact-checking system, has emerged as a new mechanism for moderating misleading information on social media and has been adopted by major platforms including X, Facebook, Instagram, Threads, and TikTok.
arXiv Machine Learning
Aug 4
Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems
arXiv:2506. 17467v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society.
arXiv AI
Jul 15
Research Novelty in Information Systems Journals After ChatGPT: Differences Across Institutional Language Contexts
arXiv:2603. 22510v2 Announce Type: replace-cross Abstract: Large language models are increasingly used in scholarly work, yet it remains unclear whether their productivity gains are accompanied by changes in research novelty.
arXiv Computation and Language
Sep 18
Social Simulacra in the Wild: AI Agent Communities on Moltbook
The paper reports 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. It finds that Moltbook shows extreme participation inequality (Gini = 0.84 vs. 0.47) and high cross‑community author overlap (33.8% vs. 0.5%). Linguistically, AI‑generated content is emotionally flattened, more assertive than exploratory, and socially detached, leading to community‑level homogenization that is largely a structural artifact of shared authorship. At the individual level, AI agents are more identifiable than human users due to outlier stylistic profiles amplified by their extreme posting volume.
arXiv AI
4d ago
The Uneven Decline of Collective Knowledge Production: Evidence from Stack Overflow After Generative AI
The study examines how the release of ChatGPT-3.5 affected collective knowledge on Stack Overflow, analyzing over two million questions from 2020 to 2025. It finds that easy questions declined sharply while difficult ones increased, with rising code complexity. Data-rich topics lost share of questions, whereas data-scarce ones gained, and these patterns held across multiple programming languages.
arXiv AI
Jul 24
Scientific exploration, collaboration and labor division in the large language model era
arXiv:2607. 20923v1 Announce Type: cross Abstract: Large language models (LLMs) have rapidly and significantly entered scientific workflows, but it remains unclear how their diffusion is associated with changes in scientists' strategies in research directions and team building.