Stereohoax: a multilingual corpus of racial hoaxes and social media reactions annotated for stereotypes.
Stereotypes have been studied extensively in the fields of social psychology and, especially with the recent advances in technology, in computational linguistics. Stereotypes have also gained even more attention nowadays because of a notable rise in their dissemination due to demographic changes and...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 3; pp. 2031 - 2070 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Sep2025
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=186909052&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186909052 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2025 vid: 59 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 186909052 10.1007/s10579-024-09791-3 ppf: 2031 ppct: 39 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.1MB tig: atl: Stereohoax: a multilingual corpus of racial hoaxes and social media reactions annotated for stereotypes. aug: au: Schmeisser-Nieto, Wolfgang S. Cignarella, Alessandra Teresa Bourgeade, Tom Frenda, Simona Ariza-Casabona, Alejandro Laurent, Mario Cicirelli, Paolo Giovanni Marra, Andrea Corbelli, Giuseppe Benamara, Farah Bosco, Cristina Moriceau, Véronique Paciello, Marinella Patti, Viviana Taulé, Mariona D'Errico, Francesca affil: https://ror.org/021018s57 Centre de Llenguatge i Computació (CLiC), Universitat de Barcelona, Barcelona, Spain https://ror.org/048tbm396 Dipartimento di Informatica, Università di Torino, Turin, Italy https://ror.org/00cv9y106 LT3, Ghent University, Ghent, Belgium https://ror.org/004raaa70 IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3, Toulouse, France https://ror.org/04vfs2w97 CNRS, Inria, LORIA, Université de Lorraine, Nancy, France https://ror.org/04mghma93 Interaction Lab, Heriot-Watt University, Edinburgh, Scotland https://ror.org/027ynra39 Dipartimento ForPsiCom, Formazione, Psicologia, Comunicazione, Università di Bari "Aldo Moro", Bari, Italy https://ror.org/00m3mb357 IPAL, CNRS-NUS-A*STAR, Singapore, Singapore https://ror.org/04q0nep37 Facoltà di Psicologia, Università Telematica Internazionale UniNettuno, Rome, Italia su: Stereotypes Emigration & immigration Social media Annotations Cross-language information retrieval Hoaxes Machine learning Language models sug: subj: Stereotypes Emigration & immigration Social media Annotations Cross-language information retrieval Hoaxes Machine learning Language models keyword: Corpus analysis Immigration Natural language processing Racial Hoax Social psychology Stereotype detection ab: Stereotypes have been studied extensively in the fields of social psychology and, especially with the recent advances in technology, in computational linguistics. Stereotypes have also gained even more attention nowadays because of a notable rise in their dissemination due to demographic changes and world events. This paper focuses on ethnic stereotypes related to immigration and presents the StereoHoax corpus, a multilingual dataset of 17,814 tweets in French, Italian, and Spanish. The corpus includes conversational threads reporting on and responding to racial hoaxes about immigrants, which we define as false claims of unlawful actions attributed to specific ethnic groups. This work describes the data collection process and the fine-grained annotation scheme we used, which is based mainly on the Stereotype Content Model adapted to the study applied to immigrants of Bosco et al. (2023). Quantitative and qualitative analyses show the distribution and correlation of annotated categories across languages, revealing, for instance, intercultural differences in the expression of stereotypes through forms of discredit. To validate our data, we performed four machine learning experiments using pre-trained BERT-like models in order to lay a foundation for automatic stereotype detection research. Leveraging the StereoHoax corpus, we gained crucial insights into the importance of context, especially in relation to the detection of implicit stereotypes. Overall, we believe that the StereoHoax corpus will prove to be a valuable resource for the automatic detection of stereotypes regarding immigrants and the study of the linguistic and psychological patterns associated with their dissemination. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
|---|