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Communication Dans Un Congrès Année : 2023

A Multilingual Dataset of Racial Stereotypes in Social Media Conversational Threads

Résumé

In this paper, we focus on the topics of mis-information 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 et al., 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 crosslingual 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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Dates et versions

hal-04122253 , version 1 (08-06-2023)

Identifiants

  • HAL Id : hal-04122253 , version 1

Citer

Tom Bourgeade, Alessandra Teresa Cignarella, Simona Frenda, Mario Laurent, Wolfgang S. Schmeisser-Nieto, et al.. A Multilingual Dataset of Racial Stereotypes in Social Media Conversational Threads. Findings of the Association for Computational Linguistics (EACL 2023), ACL: Association for Computational Linguistics, May 2023, DubrovniK, Croatia. pp.686-696. ⟨hal-04122253⟩
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