Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News

Danielly Sorato, Diana Zavala-Rojas, Maria del Carme Colominas Ventura


Abstract
The current study provides a diachronic analysis of the stereotypical portrayals concerning seven of the most prominent foreign nationalities living in Spain in a Spanish news outlet. We use 12 years (2007-2018) of news articles to train word embedding models to quantify the association of such outgroups with drug use, prostitution, crimes, and poverty concepts. Then, we investigate the effects of sociopolitical variables on the computed bias series, such as the outgroup size in the host country and the rate of the population receiving unemployment benefits. Our findings indicate that the texts exhibit bias against foreign-born people, especially in the case of outgroups for which the country of origin has a lower Gross Domestic Product per capita (PPP) than Spain.
Anthology ID:
2021.alta-1.4
Volume:
Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association
Month:
December
Year:
2021
Address:
Online
Venue:
ALTA
SIG:
Publisher:
Australasian Language Technology Association
Note:
Pages:
34–46
Language:
URL:
https://aclanthology.org/2021.alta-1.4
DOI:
Bibkey:
Cite (ACL):
Danielly Sorato, Diana Zavala-Rojas, and Maria del Carme Colominas Ventura. 2021. Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News. In Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association, pages 34–46, Online. Australasian Language Technology Association.
Cite (Informal):
Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News (Sorato et al., ALTA 2021)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.alta-1.4.pdf