Using word embeddings for immigrant and refugee stereotype quantification in a diachronic and multilingual setting
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- dc.contributor.author Sorato, Danielly
- dc.contributor.author Lundsteen, Martin
- dc.contributor.author Colominas Ventura, Carme
- dc.contributor.author Zavala-Rojas, Diana
- dc.date.accessioned 2024-03-25T06:57:16Z
- dc.date.available 2024-03-25T06:57:16Z
- dc.date.issued 2024
- dc.description.abstract Word embeddings are efficient machine-learning-based representations of human language used in many Natural Language Processing tasks nowadays. Due to their ability to learn underlying word association patterns present in large volumes of data, it is possible to observe various sociolinguistic phenomena in the embedding semantic space, such as social stereotypes. The use of stereotypical framing in discourse can be detrimental and induce misconceptions about certain groups, such as immigrants and refugees, especially when used by media and politicians in public discourse. In this paper, we use word embeddings to investigate immigrant and refugee stereotypes in a multilingual and diachronic setting. We analyze the Danish, Dutch, English, and Spanish portions of four different multilingual corpora of political discourse, covering the 1997–2018 period. Then, we measure the effect of sociopolitical variables such as the number of offences committed and the size of the refugee and immigrant groups in the host country over our measurements of stereotypical association using the Bayesian multilevel framework. Our results indicate the presence of stereotypical associations towards both immigrants and refugees for all 4 languages, and that the immigrants are overall more strongly associated with the stereotypical frames than refugees.
- dc.format.mimetype application/pdf
- dc.identifier.citation Sorato D, Lundsteen M, Colominas C, Zavala-Rojas D. Using word embeddings for immigrant and refugee stereotype quantification in a diachronic and multilingual setting. J Comput Soc Sc. 2024;7:469-521. DOI: 10.1007/s42001-023-00243-6
- dc.identifier.doi http://dx.doi.org/10.1007/s42001-023-00243-6
- dc.identifier.issn 2432-2725
- dc.identifier.uri http://hdl.handle.net/10230/59542
- dc.language.iso eng
- dc.publisher Springer
- dc.relation.ispartof Journal of Computational Social Science. 2024;7:469-521
- dc.rights 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/.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Word embeddings
- dc.subject.keyword Computational sociolinguistics
- dc.subject.keyword Social bias
- dc.subject.keyword Stereotypes
- dc.subject.keyword Diachronic analysis
- dc.subject.keyword Multilingual analysis
- dc.title Using word embeddings for immigrant and refugee stereotype quantification in a diachronic and multilingual setting
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion