Cargando…

ASIA: Automated Social Identity Assessment using linguistic style

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, h...

Descripción completa

Detalles Bibliográficos
Autores principales: Koschate, Miriam, Naserian, Elahe, Dickens, Luke, Stuart, Avelie, Russo, Alessandra, Levine, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367904/
https://www.ncbi.nlm.nih.gov/pubmed/33575985
http://dx.doi.org/10.3758/s13428-020-01511-3
_version_ 1783739111015710720
author Koschate, Miriam
Naserian, Elahe
Dickens, Luke
Stuart, Avelie
Russo, Alessandra
Levine, Mark
author_facet Koschate, Miriam
Naserian, Elahe
Dickens, Luke
Stuart, Avelie
Russo, Alessandra
Levine, Mark
author_sort Koschate, Miriam
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-020-01511-3.
format Online
Article
Text
id pubmed-8367904
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Springer US
record_format MEDLINE/PubMed
spelling pubmed-83679042021-08-31 ASIA: Automated Social Identity Assessment using linguistic style Koschate, Miriam Naserian, Elahe Dickens, Luke Stuart, Avelie Russo, Alessandra Levine, Mark Behav Res Methods Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-020-01511-3. Springer US 2021-02-11 2021 /pmc/articles/PMC8367904/ /pubmed/33575985 http://dx.doi.org/10.3758/s13428-020-01511-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Koschate, Miriam
Naserian, Elahe
Dickens, Luke
Stuart, Avelie
Russo, Alessandra
Levine, Mark
ASIA: Automated Social Identity Assessment using linguistic style
title ASIA: Automated Social Identity Assessment using linguistic style
title_full ASIA: Automated Social Identity Assessment using linguistic style
title_fullStr ASIA: Automated Social Identity Assessment using linguistic style
title_full_unstemmed ASIA: Automated Social Identity Assessment using linguistic style
title_short ASIA: Automated Social Identity Assessment using linguistic style
title_sort asia: automated social identity assessment using linguistic style
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367904/
https://www.ncbi.nlm.nih.gov/pubmed/33575985
http://dx.doi.org/10.3758/s13428-020-01511-3
work_keys_str_mv AT koschatemiriam asiaautomatedsocialidentityassessmentusinglinguisticstyle
AT naserianelahe asiaautomatedsocialidentityassessmentusinglinguisticstyle
AT dickensluke asiaautomatedsocialidentityassessmentusinglinguisticstyle
AT stuartavelie asiaautomatedsocialidentityassessmentusinglinguisticstyle
AT russoalessandra asiaautomatedsocialidentityassessmentusinglinguisticstyle
AT levinemark asiaautomatedsocialidentityassessmentusinglinguisticstyle