## 2 Related Work
However, stereotypes are not always merely the association of _words_ (seen as attributes or characteristics) to two opposite social groups (e.g., women vs. men), and it is not always clear to define the opposite groups by using specific keywords, for instance in the case of immigrants vs. nonimmigrants, the set of words to represent nonimmigrants is not clear. There are few works related to the detection of stereotypes about immigrants. In [46], a system is proposed that allows one to see what was said about Muslims and Dutch people. The authors use the collection of descriptions that a single text provides on a given entity or event (it was called _microportraits_: labels, descriptions, properties, roles). For example, the expression _the pions Muslim smiled_ contains the label _Muslim_, the property _pious_, and the role _smiling_. This is an interesting study that helps explain how prejudice works according to social psychologists. In [30] an Italian dataset was created that focused on hate speech against immigrants, that included the annotation _{yes, no}_ about whether a tweet is a (mostly untrue) stereotype or not. In the HaSpecDe shared task at EVALITA 2020 [47], six teams submitted their results for the stereotype detection task in addition to their hate speech models, using the above dataset. Most of those teams only adapted the same hate speech model to stereotype identification, representing (and reducing) stereotypes to characteristics of hate speech. The authors of [47] observed that stereotype appears as a more subtle phenomenon that needs to be approached also as nonhurtful text. From a psychosocial perspective, the better-established model to analyze the language that shapes a group of stereotypes is the Stereotype Content Model (SCM) developed by [PERSON] and colleagues [48; 49; 50]. [PERSON] has developed his model arguing that in encounters with conspecifics, social animals--i.e., humans--must determine, immediately, whether the \"other\" is friend or foe (i.e., intends good or ill) and, then, whether the \"other\" can enact those intentions. Authors affirm that in answering these questions, humans use two universal dimensions of social cognition--warmth and competence--to judge individuals and groups. People perceived as warm and competent elicit uniformly positive emotions and behaviour, whereas those perceived as lacking warmth and competence elicit uniform negativity. People classified as high on one dimension and low on the other elicit predictable, ambivalent affective and behavioural reactions. This theoretical framework has been completed with the ambivalent stereotypes hypothesis: many groups are stereotyped as high in one dimension and low in the other [48]. [PERSON], [PERSON] and [PERSON] [51] also have investigated how stereotypes and emotions shape behavioural tendencies toward different groups and have proposed the BIAS Map (Behaviors from Intergroup Affect and Stereotypes).They did a correlation study with a representative US sample and conclude that warmth stereotypes determine active behavioural tendencies--attenuating active harm (harassing) and eliciting active facilitation (helping). Competence stereotypes determine passive behavioural tendencies--attenuating passive harm (neglecting) and eliciting passive facilitation (associating). Admired groups (warm, competent) elicit both facilitation tendencies; hated groups (cold, incompetent) elicit both harm tendencies. Envied groups (competent, cold) elicit passive facilitation but active harm and pitied groups (warm, incompetent) elicit active facilitation but passive harm. In this research, the authors also find that emotions predict behavioural tendencies more strongly than stereotypes do and usually mediate stereotype-behavioural-tendency links. In this research [51] immigrants are placed between the set of groups that are seen as \"low warmth and low competence\", with other social groups seen as poor, homeless, including Latinos, Muslims, and Africans, in the particular US context.