ASIA: Automated Social Identity Assessment using linguistic style

Autor: Elahe Naserian, Mark Levine, Miriam Koschate, Avelie Stuart, Alessandra Russo, Luke Dickens
Přispěvatelé: Engineering & Physical Science Research Council (EPSRC)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
media_common.quotation_subject
1702 Cognitive Sciences
Emotions
Social Sciences
050109 social psychology
Experimental and Cognitive Psychology
Social Environment
050105 experimental psychology
Article
Style (sociolinguistics)
Psychology
Mathematical
Social media data
Arts and Humanities (miscellaneous)
Developmental and Educational Psychology
0801 Artificial Intelligence and Image Processing
Psychology
Humans
0501 psychology and cognitive sciences
Quality (business)
Social identity theory
Psychological assessment
General Psychology
media_common
Protocol (science)
Social Identification
Psychology
Experimental
Field (Bourdieu)
Natural language processing
05 social sciences
Social identity
Linguistics
Experimental Psychology
Social relation
Variety (cybernetics)
Salient
1701 Psychology
Social categorization
Psychology (miscellaneous)
Zdroj: Behavior Research Methods
Popis: 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.
Databáze: OpenAIRE
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