发表机构
University of Colorado Boulder（科罗拉多大学博尔德分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过Bluesky数据，结合网络SIR模型，分析AI事件（如DeepSeek R1发布）在不同群体中的参与动态，发现直接响应与网络传播强度不总一致，揭示群体参与模式的差异。
AI 中文摘要
社交媒体日益塑造日常生活，既是重大事件讨论的核心场所，也是在线集体行为可能溢出至现实世界的空间，而人工智能（AI）同样在社会中日益具有影响力。因此，理解不同在线社区如何回应AI相关事件，并厘清此类参与度发展的潜在机制，对于研究信息传播和社会对AI的接受度愈发重要。我们的工作旨在弥合AI相关在线参与的实证研究与群体层面传播动态的数学建模之间的有限联系。我们开发了一个框架，将实证的Bluesky活动收集并组织为不同的用户群体，然后使用基于网络的动力学模型来探究其参与度背后的机制。我们以DeepSeek R1的发布为案例研究，采用两种互补的分组方案：AI相关社区和学术学科。对于每个群体，我们拟合了一个带有外生参与项的基于网络的易感-感染-恢复（SIR）型模型，从而能够量化内生网络驱动传播与直接外部响应的相对强度。跨群体来看，我们发现对事件的强烈直接响应并不一定与强烈的网络驱动传播同时出现，揭示了仅凭总体活动可能掩盖的不同参与模式。
英文摘要
Social media increasingly shapes everyday life, serving as both a central venue for discussion of major events and a space where online collective behavior can spill over into real-world activity, while artificial intelligence (AI) is likewise becoming increasingly influential across society. Understanding how different online communities respond to AI-related events, and disentangling the mechanisms underlying the development of such engagement, are therefore increasingly important for studying information spreading and the societal reception of AI. Our work addresses the limited connection between empirical studies of AI-related online engagement and mathematical modeling of group-level spreading dynamics. We develop a framework to collect and organize empirical Bluesky activity into distinct user groups, then use a network-based dynamics model to investigate the mechanisms underlying their engagement. We use the release of DeepSeek R1 as a case study under two complementary grouping schemes: AI-related communities and academic disciplines. For each group, we fit a network-based Susceptible-Infected-Recovered (SIR)-type model augmented with an exogenous engagement term, allowing us to quantify the relative strengths of endogenous network-driven spreading and direct external response. Across groups, we find that strong direct responses to the event do not necessarily coincide with strong network-driven propagation, revealing distinct engagement patterns that may be obscured by aggregate activity alone.
Comments10 pages, 4 figures, 3 tables
