When the social boundaries between groups are breached, the tendency for people to erect and maintain symbolic boundaries intensifies. Drawing on extant perspectives on boundary maintenance, we distinguish between two strategies that people pursue in maintaining symbolic boundaries: boundary retention—entrenching themselves in pre-existing symbolic distinctions—and boundary reformation—innovating new forms of symbolic distinction. Traditional approaches to measuring symbolic boundaries—interviews, participant-observation, and self-reports are ill-suited to detecting fine-grained variation in boundary maintenance. To overcome this limitation, we use the tools of computational linguistics and machine learning to develop a novel approach to measuring symbolic boundaries based on interactional language use between group members before and after they encounter one another. We construct measures of boundary retention and reformation using random forest classifiers that quantify group differences based on pre- and post-contact linguistic styles. We demonstrate this method's utility by applying it to a corpus of email communications from a mid-sized financial services firm that acquired and integrated two smaller firms. We find that: (a) the persistence of symbolic boundaries can be detected for up to 18 months after a merger; (b) acquired employees exhibit more boundary reformation and less boundary retention than their counterparts from the acquiring firm; and (c) individuals engage in more boundary retention, but not reformation, when their local work environment is more densely populated by ingroup members. We discuss implications of these findings for the study of culture in a wide range of intergroup contexts and for computational approaches to measuring culture.

中文翻译：

---

一种基于语言的社会群体间符号边界维持评估方法

当群体之间的社会界限被打破时，人们建立和维护象征性界限的趋势就会加强。借鉴现有的关于边界维护的观点，我们区分了人们在维护符号边界时所追求的两种策略：边界保留——在预先存在的符号区别中巩固自己——和边界改革——创新符号区别的新形式。测量符号边界的传统方法——访谈、参与者观察和自我报告不适合检测边界维护中的细粒度变化。为了克服这个限制，我们使用计算语言学和机器学习的工具开发了一种新的方法来测量符号边界，该方法基于群体成员在彼此相遇之前和之后的交互语言使用。我们使用随机森林分类器构建边界保留和重构的度量，该分类器基于接触前和接触后的语言风格量化群体差异。我们通过将这种方法应用于来自一家收购并整合了两家较小公司的中型金融服务公司的电子邮件通信语料库来展示这种方法的实用性。我们发现： (a) 符号边界的持久性可以在合并后长达 18 个月内被检测到；(b) 与收购公司的同行相比，被收购的员工表现出更多的边界改革和更少的边界保留；(c) 当当地工作环境中的内群体成员更密集时，个人会参与更多的边界保留，而不是改革。我们讨论了这些发现对广泛的群体间背景下的文化研究以及测量文化的计算方法的影响。