社交媒体中生活事件冲击下社会关系参与度的分析
Analyzing the Engagement of Social Relationships During Life Event Shocks in Social Media
AI总结：
本文通过分析Twitter上超过13K个冲击事件和110K条回复，揭示了不同社会关系类型在在线冲击响应中的独特模式，发现关系强度与结构嵌入性的影响取决于关系类型和冲击类型。
摘要：
经历意外痛苦事件（即冲击）的个体通常依赖其社交网络获取支持。尽管已有研究展示了社交网络如何对冲击做出响应，但这些研究通常平等对待所有关系，忽略了不同社会关系所提供的支持存在差异。本文在Twitter上进行了一项计算分析，考察在线冲击响应如何因用户二元关系的类型而异。我们引入了一个新数据集，包含超过13,000个个体在Twitter上自我报告冲击事件的实例，并围绕这些事件构建了带有关系标签的二元互动网络。通过在一项伪因果分析中考察对受冲击用户的110,000条回复行为，我们展示了响应水平和话题转移方面的关系特定模式。我们还表明，尽管亲密度等成熟的社会维度（如关系强度和结构嵌入性）有助于冲击响应性，但其影响程度高度依赖于关系类型和冲击类型。我们的研究结果表明，社会关系在网络互动中具有高度独特的特征，且在线冲击响应中的关系特定行为与线下情境中的行为存在差异。
英文摘要：
Individuals experiencing unexpected distressing events, shocks, often rely on their social network for support. While prior work has shown how social networks respond to shocks, these studies usually treat all ties equally, despite differences in the support provided by different social relationships. Here, we conduct a computational analysis on Twitter that examines how responses to online shocks differ by the relationship type of a user dyad. We introduce a new dataset of over 13K instances of individuals' self-reporting shock events on Twitter and construct networks of relationship-labeled dyadic interactions around these events. By examining behaviors across 110K replies to shocked users in a pseudo-causal analysis, we demonstrate relationship-specific patterns in response levels and topic shifts. We also show that while well-established social dimensions of closeness such as tie strength and structural embeddedness contribute to shock responsiveness, the degree of impact is highly dependent on relationship and shock types. Our findings indicate that social relationships contain highly distinctive characteristics in network interactions and that relationship-specific behaviors in online shock responses are unique from those of offline settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
