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dc.contributor.author | Sánchez-Junquera, Javier | es_ES |
dc.contributor.author | Chulvi-Ferriols, María Alberta | es_ES |
dc.contributor.author | Rosso, Paolo | es_ES |
dc.contributor.author | Ponzetto, Simone Paolo | es_ES |
dc.date.accessioned | 2022-05-16T18:02:38Z | |
dc.date.available | 2022-05-16T18:02:38Z | |
dc.date.issued | 2021-04 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/182637 | |
dc.description.abstract | [EN] Stereotype is a type of social bias massively present in texts that computational models use. There are stereotypes that present special difficulties because they do not rely on personal attributes. This is the case of stereotypes about immigrants, a social category that is a preferred target of hate speech and discrimination. We propose a new approach to detect stereotypes about immigrants in texts focusing not on the personal attributes assigned to the minority but in the frames, that is, the narrative scenarios, in which the group is placed in public speeches. We have proposed a fine-grained social psychology grounded taxonomy with six categories to capture the different dimensions of the stereotype (positive vs. negative) and annotated a novel StereoImmigrants dataset with sentences that Spanish politicians have stated in the Congress of Deputies. We aggregate these categories in two supracategories: one is Victims that expresses the positive stereotypes about immigrants and the other is Threat that expresses the negative stereotype. We carried out two preliminary experiments: first, to evaluate the automatic detection of stereotypes; and second, to distinguish between the two supracategories of immigrants¿ stereotypes. In these experiments, we employed state-of-the-art transformer models (monolingual and multilingual) and four classical machine learning classifiers. We achieve above 0.83 of accuracy with the BETO model in both experiments, showing that transformers can capture stereotypes about immigrants with a high level of accuracy. | es_ES |
dc.description.sponsorship | The work of the authors from the Universitat Politecnica de Valencia was funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). The work of Paolo Rosso was done also in the framework of the research project PROMETEO/2019/121 (DeepPattern) funded by the Generalitat Valenciana. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Applied Sciences | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Social bias | es_ES |
dc.subject | Stereotypes about immigrants | es_ES |
dc.subject | Social psychology based taxonomy | es_ES |
dc.subject | StereoImmigrants dataset | es_ES |
dc.subject | Transformer models | es_ES |
dc.subject | Spanish | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | How Do You Speak about Immigrants? Taxonomy and StereoImmigrants Dataset for Identifying Stereotypes about Immigrants | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/app11083610 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-096212-B-C31/ES/DESINFORMACION Y AGRESIVIDAD EN SOCIAL MEDIA: AGREGANDO INFORMACION Y ANALIZANDO EL LENGUAJE/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//PROMETEO%2F2019%2F121//DEEP LEARNING FOR ADAPTATIVE AND MULTIMODAL INTERACTION IN PATTERN RECOGNITION/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.description.bibliographicCitation | Sánchez-Junquera, J.; Chulvi-Ferriols, MA.; Rosso, P.; Ponzetto, SP. (2021). How Do You Speak about Immigrants? Taxonomy and StereoImmigrants Dataset for Identifying Stereotypes about Immigrants. Applied Sciences. 11(8):1-27. https://doi.org/10.3390/app11083610 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/app11083610 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 27 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 11 | es_ES |
dc.description.issue | 8 | es_ES |
dc.identifier.eissn | 2076-3417 | es_ES |
dc.relation.pasarela | S\437472 | es_ES |
dc.contributor.funder | GENERALITAT VALENCIANA | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |