发表机构
University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出“价值面孔”系统，基于施瓦茨价值观分析聊天历史，生成不同关系的价值观档案，以两倍于随机猜测的准确率区分关系情境，并帮助用户反思自我呈现的差异。
AI 中文摘要
人们在不同的关系中会展现自身的不同方面。计算工作已经捕捉到了沟通风格中的这种变化。但这种变化还延伸到了人们在特定关系中所强调或淡化的原则——即他们所表达的价值观以及如何平衡这些价值观。我们将这些特定于关系的价值观表达概念化为“展现的价值观”。为了使展现的价值观可见，我们引入了“价值面孔”（Value Faces），这是一个利用施瓦茨的十种基本人类价值观来分析人们日常消息平台中现有聊天历史的系统，并为他们的不同关系生成单独的价值观档案。在一项混合方法研究（N=18）中，我们发现生成的价值观档案以两倍于猜测的几率区分了参与者的关系情境，而系统推断出的跨关系差异与参与者对这些差异的感知一致。参与者利用这些档案来阐明他们自我呈现中先前隐含的差异，将这些差异与角色和随时间的变化联系起来，并重新考虑他们的自我评估。
英文摘要
People present different aspects of themselves across relationships. Computational work has captured such variation in communication style. But this variation also extends to which principles people foreground or background in a particular relationship--i.e., in the values they express and how they balance them. We conceptualize these relationship-specific expressions of values as demonstrated values. To make demonstrated values visible, we introduce Value Faces, a system that analyzes a person's existing chat histories from their everyday messaging platforms using Schwartz's ten basic human values and produces separate value profiles for their different relationships. In a mixed-methods study(N=18), we find that the resulting value profiles distinguished participants' relational contexts with twice the odds of guessing, while system-inferred differences across relationships aligned with participants' perceptions of those differences. Participants used these profiles to articulate previously implicit differences in how they presented themselves, connect them to roles and changes over time, and reconsider their self-assessments.
