@inproceedings{bourgeade-etal-2023-multilingual,
title = "A Multilingual Dataset of Racial Stereotypes in Social Media Conversational Threads",
author = "Bourgeade, Tom and
Cignarella, Alessandra Teresa and
Frenda, Simona and
Laurent, Mario and
Schmeisser-Nieto, Wolfgang and
Benamara, Farah and
Bosco, Cristina and
Moriceau, V{\'e}ronique and
Patti, Viviana and
Taul{\'e}, Mariona",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.51",
doi = "10.18653/v1/2023.findings-eacl.51",
pages = "686--696",
abstract = "In this paper, we focus on the topics of misinformation and racial hoaxes from a perspective derived from both social psychology and computational linguistics. In particular, we consider the specific case of anti-immigrant feeling as a first case study for addressing racial stereotypes. We describe the first corpus-based study for multilingual racial stereotype identification in social media conversational threads. Our contributions are: (i) a multilingual corpus of racial hoaxes, (ii) a set of common guidelines for the annotation of racial stereotypes in social media texts, and a multi-layered, fine-grained scheme, psychologically grounded on the work by Fiske, including not only stereotype presence, but also contextuality, implicitness, and forms of discredit, (iii) a multilingual dataset in Italian, Spanish, and French annotated following the aforementioned guidelines, and cross-lingual comparative analyses taking into account racial hoaxes and stereotypes in online discussions. The analysis and results show the usefulness of our methodology and resources, shedding light on how racial hoaxes are spread, and enable the identification of negative stereotypes that reinforce them.",
}
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<abstract>In this paper, we focus on the topics of misinformation and racial hoaxes from a perspective derived from both social psychology and computational linguistics. In particular, we consider the specific case of anti-immigrant feeling as a first case study for addressing racial stereotypes. We describe the first corpus-based study for multilingual racial stereotype identification in social media conversational threads. Our contributions are: (i) a multilingual corpus of racial hoaxes, (ii) a set of common guidelines for the annotation of racial stereotypes in social media texts, and a multi-layered, fine-grained scheme, psychologically grounded on the work by Fiske, including not only stereotype presence, but also contextuality, implicitness, and forms of discredit, (iii) a multilingual dataset in Italian, Spanish, and French annotated following the aforementioned guidelines, and cross-lingual comparative analyses taking into account racial hoaxes and stereotypes in online discussions. The analysis and results show the usefulness of our methodology and resources, shedding light on how racial hoaxes are spread, and enable the identification of negative stereotypes that reinforce them.</abstract>
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%0 Conference Proceedings
%T A Multilingual Dataset of Racial Stereotypes in Social Media Conversational Threads
%A Bourgeade, Tom
%A Cignarella, Alessandra Teresa
%A Frenda, Simona
%A Laurent, Mario
%A Schmeisser-Nieto, Wolfgang
%A Benamara, Farah
%A Bosco, Cristina
%A Moriceau, Véronique
%A Patti, Viviana
%A Taulé, Mariona
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F bourgeade-etal-2023-multilingual
%X In this paper, we focus on the topics of misinformation and racial hoaxes from a perspective derived from both social psychology and computational linguistics. In particular, we consider the specific case of anti-immigrant feeling as a first case study for addressing racial stereotypes. We describe the first corpus-based study for multilingual racial stereotype identification in social media conversational threads. Our contributions are: (i) a multilingual corpus of racial hoaxes, (ii) a set of common guidelines for the annotation of racial stereotypes in social media texts, and a multi-layered, fine-grained scheme, psychologically grounded on the work by Fiske, including not only stereotype presence, but also contextuality, implicitness, and forms of discredit, (iii) a multilingual dataset in Italian, Spanish, and French annotated following the aforementioned guidelines, and cross-lingual comparative analyses taking into account racial hoaxes and stereotypes in online discussions. The analysis and results show the usefulness of our methodology and resources, shedding light on how racial hoaxes are spread, and enable the identification of negative stereotypes that reinforce them.
%R 10.18653/v1/2023.findings-eacl.51
%U https://aclanthology.org/2023.findings-eacl.51
%U https://doi.org/10.18653/v1/2023.findings-eacl.51
%P 686-696
Markdown (Informal)
[A Multilingual Dataset of Racial Stereotypes in Social Media Conversational Threads](https://aclanthology.org/2023.findings-eacl.51) (Bourgeade et al., Findings 2023)
ACL
- Tom Bourgeade, Alessandra Teresa Cignarella, Simona Frenda, Mario Laurent, Wolfgang Schmeisser-Nieto, Farah Benamara, Cristina Bosco, Véronique Moriceau, Viviana Patti, and Mariona Taulé. 2023. A Multilingual Dataset of Racial Stereotypes in Social Media Conversational Threads. In Findings of the Association for Computational Linguistics: EACL 2023, pages 686–696, Dubrovnik, Croatia. Association for Computational Linguistics.