ASIA: Automated social identity assessment using linguistic style
dc.contributor.author | Koschate-Reis, M | |
dc.contributor.author | Naserianhanzaei, E | |
dc.contributor.author | Dickens, L | |
dc.contributor.author | Stuart, A | |
dc.contributor.author | Russo, A | |
dc.contributor.author | Levine, M | |
dc.date.accessioned | 2021-02-17T11:33:32Z | |
dc.date.issued | 2021-02-11 | |
dc.description.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. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.identifier.citation | Published online 11 February 2021 | en_GB |
dc.identifier.doi | 10.3758/s13428-020-01511-3 | |
dc.identifier.grantnumber | EP/J005053/1 | en_GB |
dc.identifier.grantnumber | EP/K033433/1 | en_GB |
dc.identifier.grantnumber | EP/S001409/1 | |
dc.identifier.grantnumber | EP/K033425/1 | |
dc.identifier.uri | http://hdl.handle.net/10871/124775 | |
dc.language.iso | en | en_GB |
dc.publisher | Psychonomic Society / Springer | en_GB |
dc.relation.url | https://github.com/Identity-lab/Tutorial-on-salient-social-Identity-detection-model | |
dc.relation.url | https://osf.io/87t6h/?view_only=a1b5afe488db4014b3f21ed808bcceb9 | |
dc.rights | © The Author(s) 2021. 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/. | en_GB |
dc.subject | Social categorization | en_GB |
dc.subject | Social identity | en_GB |
dc.subject | Natural language processing | en_GB |
dc.subject | Social media data | en_GB |
dc.subject | Psychological assessment | en_GB |
dc.title | ASIA: Automated social identity assessment using linguistic style | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2021-02-17T11:33:32Z | |
dc.identifier.issn | 1554-351X | |
dc.description | This is the final version. Available on open access from Springer via the DOI in this record. | en_GB |
dc.description | Data Availability: In the interest of open science and replicability, we provide an accessible step-by-step tutorial of how to replicate our proof-of-concept studies, which can be found on ASIA’s GitHub page (https://github.com/Identity-lab/Tutorial-on-salient-social-Identity-detection-model). The tutorial contains the Python code for preparing the datasets, and the code for training and testing the models. Data for each study, including the necessary LIWC vectors and other relevant variables for each dataset, can be found on OSF: https://osf.io/87t6h/?view_only=a1b5afe488db4014b3f21ed808bcceb9. For ethical reasons (see Step 1), the original posts cannot be shared publicly but are available upon reasonable request from the first author. | |
dc.identifier.eissn | 1554-3528 | |
dc.identifier.journal | Behavior Research Methods | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2020-11-10 | |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2021-02-11 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2021-02-17T11:30:11Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2021-02-17T11:33:40Z | |
refterms.panel | A | en_GB |
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Except where otherwise noted, this item's licence is described as © The Author(s) 2021. 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/.