## 7 Conclusions and Future Work  
In order to advance in the study of stereotypes, for instance about immigrants, social sciences need to complement the classical paradigm that focuses on how a group is defined in terms of personal attributes with a new paradigm that emphasises the _frames_, that is, the narrative scenarios, in which a group (e.g., immigrants) is mentioned. In this work, we developed a taxonomy to identify stereotypes about immigrants not from the attributes that are assigned to the group but from the narrative scenarios in which the speaker places the group (**RQ1**). This fine grained taxonomy allows for the study of stereotypes about immigrants and covers the whole spectrum of the stereotype: from positive images of the group (as equals to the nationals or victims of xenophobia) to more negative images (a threat to the nationals or a less human people). Based on psychosocial research on prejudice and stereotypes, this language-independent taxonomy is a new conceptual instrument with two objectives: (i) to provide computational linguistics with a new conceptual tool to detect and mitigate social bias in datasets, specifically, stereotypes about immigrants; and (ii) to strengthen the collaboration between social sciences and computational linguistics to understand better how stereotypes are generated in the context of public discourse. We have validated our taxonomy considering how each category is related with some attitude (pro-immigrant, anti-immigrant, or neutral). We have identified two opposite supracategories of the stereotypes about immigrants: one that presents the minority as _victim_ and the other that presents the minority as a _threat_. We annotated political speeches of the ParlSpeech V2 dataset, focusing on the speeches from 1996 to 1998, from 2006 to 2008, and from 2016 to 2018. The resulting _Stereotyping_s stereotype-annotated dataset was created relying on the new taxonomy (**RQ2**). The dataset will be made public to the research community to foster the research on stereotypes about immigrants. _Stereotyping_ was used to carry out some preliminary experiments using state-of-the-art transformer models and classical machine learning models. We obtained results between 0.70 and 0.86 of accuracy in the two experiments we performed: Stereotype vs. Nonstereotype and Victims vs. Threat. The best performance was obtained by BETO, a monolingual Spanish transformer model, suggesting that this model could be capturing a richer representation of stereotypes and their dimensions, than the classical machine learning models do (**RQ3**). We also point up that with these preliminary experiments we prove the existence of social bias, in particular stereotypes about immigrants, in political speeches, and the effectiveness of automatically detecting them. From the most relevant n-grams from the examples labelled as _Stereotype_, we confirmed that they are not trivially associated with the immigrant group (they are not always biased attributes) and, therefore, we should not rely only on a set of keywords to represent it. Additionally, we confirmed that in the study of stereotypes about immigrants, we have to consider not to define two opposite social groups, since the nonstereotypical texts are neutral phrases talking about immigration in general without stereotyping at all. We have analysed the confusion matrices considering metadata from our _Stereotyping_ dataset, in particular, the political party the speaker belongs to. This analysis has shown that the confusion could be explained because certain rhetorical strategies are particularly difficult to infer for transformers and not for humans. When speakers use the same words for different purposes, humans elaborate the different meanings of the sentence making an inference about the intention of the speaker, but transformers have difficulties inferring the rhetorical strategies. More work is necessary in this direction.