Validation factorielle d’une mesure des menaces du stéréotype en langue française

Desombre, Caroline; Jury, Mickaël; Renard, Muriel; Brasselet, Célénie

doi:10.3917/anpsy1.204.0251

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…As the authors said, "this is unfortunate and perhaps unavoidable as long as we rely on real word distribution of corpora to train language models". The difficulty is clear but the need also: these stereotypes have a strong effect on the members of the stigmatised group, for instance, impacting the performance of individuals who face stereotype threats [7][8][9]. We have known from the beginning of social psychology that stereotypes are at the base of prejudice towards minorities and to spread prejudices is an efficient strategy for dogmatic groups and authoritarian ideologies [10,11].…”

Section: Introductionmentioning

confidence: 99%

How Do You Speak about Immigrants? Taxonomy and StereoImmigrants Dataset for Identifying Stereotypes about Immigrants

et al. 2021

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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.

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