Language Style Matching as a Predictor of Social Dynamics in Small Groups
Language Style Matching as a Predictor of Social Dynamics in Small Groups
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DOI：
10.1177/0093650209351468
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发表时间：
2010-02-01
影响因子：
6.2
通讯作者：
Pennebaker, James W.
中科院分区：
文献类型：
--
作者：
Gonzales, Amy L.;Hancock, Jeffrey T.;Pennebaker, James W.
同步言语行为可以揭示有关社会动态的重要信息。本研究介绍了基于功能词自动文本分析的语言风格匹配（LSM）算法，用于计算言语模仿。 LSM 算法应用于小组讨论期间生成的语言，其中 70 个小组由 324 人组成，面对面或通过基于文本的计算机介导的交流参与信息搜索任务。作为一种指标，LSM 预测了两种通信环境中群体的凝聚力，并预测了面对面群体中的任务绩效。其他语言特征也与小组的凝聚力和表现有关，包括字数、代词模式和动词时态。结果表明，这种语言模仿的自动测量可以成为预测潜在社会动态的客观、有效且不引人注目的工具。总的来说，该研究证明了使用语言来预测感兴趣的社会心理因素变化的有效性。
Synchronized verbal behavior can reveal important information about social dynamics. This study introduces the linguistic style matching (LSM) algorithm for calculating verbal mimicry based on an automated textual analysis of function words. The LSM algorithm was applied to language generated during a small group discussion in which 70 groups comprised of 324 individuals engaged in an information search task either face-to-face or via text-based computer-mediated communication. As a metric, LSM predicted the cohesiveness of groups in both communication environments, and it predicted task performance in face-to-face groups. Other language features were also related to the groups' cohesiveness and performance, including word count, pronoun patterns, and verb tense. The results reveal that this type of automated measure of verbal mimicry can be an objective, efficient, and unobtrusive tool for predicting underlying social dynamics. In total, the study demonstrates the effectiveness of using language to predict change in social psychological factors of interest.