Department of Psychology, University of Exeter, Washington Singer Laboratories, Exeter, EX4 4QG, UK.
Institute for Data Science and AI, University of Exeter, Exeter, UK.
Behav Res Methods. 2021 Aug;53(4):1762-1781. doi: 10.3758/s13428-020-01511-3. Epub 2021 Feb 11.
The various group and category memberships that we hold are at the heart of who we are. They have been shown to affect our thoughts, emotions, behavior, and social relations in a variety of social contexts, and have more recently been linked to our mental and physical well-being. Questions remain, however, over the dynamics between different group memberships and the ways in which we cognitively and emotionally acquire these. In particular, current assessment methods are missing that can be applied to naturally occurring data, such as online interactions, to better understand the dynamics and impact of group memberships in naturalistic settings. To provide researchers with a method for assessing specific group memberships of interest, we have developed ASIA (Automated Social Identity Assessment), an analytical protocol that uses linguistic style indicators in text to infer which group membership is salient in a given moment, accompanied by an in-depth open-source Jupyter Notebook tutorial ( https://github.com/Identity-lab/Tutorial-on-salient-social-Identity-detection-model ). Here, we first discuss the challenges in the study of salient group memberships, and how ASIA can address some of these. We then demonstrate how our analytical protocol can be used to create a method for assessing which of two specific group memberships-parents and feminists-is salient using online forum data, and how the quality (validity) of the measurement and its interpretation can be tested using two further corpora as well as an experimental study. We conclude by discussing future developments in the field.
我们所拥有的各种群体和类别成员身份是我们的核心所在。这些身份在各种社会环境中影响着我们的思想、情感、行为和社会关系，最近还与我们的身心健康联系在一起。然而，不同群体成员之间的动态关系以及我们认知和情感上获得这些关系的方式仍存在问题。特别是，目前缺少可以应用于自然发生数据（如在线交互）的评估方法，以更好地了解自然环境中群体成员身份的动态和影响。为了为研究人员提供一种评估特定群体成员身份的方法，我们开发了 ASIA（自动社会身份评估），这是一种分析协议，它使用文本中的语言风格指标来推断在给定时刻哪个群体成员身份是突出的，同时还提供了一个深入的开源 Jupyter Notebook 教程（https://github.com/Identity-lab/Tutorial-on-salient-social-Identity-detection-model）。在这里，我们首先讨论了研究突出群体成员身份的挑战，以及 ASIA 如何解决其中的一些挑战。然后，我们展示了如何使用我们的分析协议来创建一种方法，用于使用在线论坛数据评估两个特定群体成员身份（父母和女权主义者）中的哪一个是突出的，以及如何使用另外两个语料库和一项实验研究来测试测量的质量（有效性）及其解释。最后，我们讨论了该领域的未来发展。
