Computational Modeling of Stereotype Content in Text

Stereotypes are encountered every day, in interpersonal communication as well as in entertainment, news stories, and on social media. In this study, we present a computational method to mine large, naturally occurring datasets of text for sentences that express perceptions of a social group of inter...
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Gespeichert in:

Autor*in:	Kathleen C. Fraser [verfasserIn] Svetlana Kiritchenko [verfasserIn] Isar Nejadgholi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	stereotypes natural language processing computational social science computational model sentence embeddings social media analysis

Übergeordnetes Werk:	In: Frontiers in Artificial Intelligence - Frontiers Media S.A., 2019, 5(2022)
Übergeordnetes Werk:	volume:5 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/frai.2022.826207

Katalog-ID:	DOAJ025331280

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callnumber-first	Q - Science
author	Kathleen C. Fraser
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topic	misc QA75.5-76.95 misc stereotypes misc natural language processing misc computational social science misc computational model misc sentence embeddings misc social media analysis misc Electronic computers. Computer science
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title	Computational Modeling of Stereotype Content in Text
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title_auth	Computational Modeling of Stereotype Content in Text
abstract	Stereotypes are encountered every day, in interpersonal communication as well as in entertainment, news stories, and on social media. In this study, we present a computational method to mine large, naturally occurring datasets of text for sentences that express perceptions of a social group of interest, and then map these sentences to the two-dimensional plane of perceived warmth and competence for comparison and interpretation. This framework is grounded in established social psychological theory, and validated against both expert annotation and crowd-sourced stereotype data. Additionally, we present two case studies of how the model might be used to answer questions using data “in-the-wild,” by collecting Twitter data about women and older adults. Using the data about women, we are able to observe how sub-categories of women (e.g., Black women and white women) are described similarly and differently from each other, and from the superordinate group of women in general. Using the data about older adults, we show evidence that the terms people use to label a group (e.g., old people vs. senior citizens) are associated with different stereotype content. We propose that this model can be used by other researchers to explore questions of how stereotypes are expressed in various large text corpora.
abstractGer	Stereotypes are encountered every day, in interpersonal communication as well as in entertainment, news stories, and on social media. In this study, we present a computational method to mine large, naturally occurring datasets of text for sentences that express perceptions of a social group of interest, and then map these sentences to the two-dimensional plane of perceived warmth and competence for comparison and interpretation. This framework is grounded in established social psychological theory, and validated against both expert annotation and crowd-sourced stereotype data. Additionally, we present two case studies of how the model might be used to answer questions using data “in-the-wild,” by collecting Twitter data about women and older adults. Using the data about women, we are able to observe how sub-categories of women (e.g., Black women and white women) are described similarly and differently from each other, and from the superordinate group of women in general. Using the data about older adults, we show evidence that the terms people use to label a group (e.g., old people vs. senior citizens) are associated with different stereotype content. We propose that this model can be used by other researchers to explore questions of how stereotypes are expressed in various large text corpora.
abstract_unstemmed	Stereotypes are encountered every day, in interpersonal communication as well as in entertainment, news stories, and on social media. In this study, we present a computational method to mine large, naturally occurring datasets of text for sentences that express perceptions of a social group of interest, and then map these sentences to the two-dimensional plane of perceived warmth and competence for comparison and interpretation. This framework is grounded in established social psychological theory, and validated against both expert annotation and crowd-sourced stereotype data. Additionally, we present two case studies of how the model might be used to answer questions using data “in-the-wild,” by collecting Twitter data about women and older adults. Using the data about women, we are able to observe how sub-categories of women (e.g., Black women and white women) are described similarly and differently from each other, and from the superordinate group of women in general. Using the data about older adults, we show evidence that the terms people use to label a group (e.g., old people vs. senior citizens) are associated with different stereotype content. We propose that this model can be used by other researchers to explore questions of how stereotypes are expressed in various large text corpora.
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title_short	Computational Modeling of Stereotype Content in Text
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