Koschate, M;
Naserian, E;
Dickens, L;
Stuart, A;
Russo, A;
Levine, M;
(2021)
ASIA: Automated Social Identity Assessment using linguistic style.
Behavior Research Methods
10.3758/s13428-020-01511-3.
(In press).
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | ASIA: Automated Social Identity Assessment using linguistic style |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3758/s13428-020-01511-3 |
| Publisher version: | https://doi.org/10.3758/s13428-020-01511-3 |
| Language: | English |
| Additional information: | © 2021 Springer Nature Switzerland AG. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Social categorization, Social identity, Natural language processing, Social media data, Psychological assessment |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities > Dept of Information Studies |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10122845 |
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