The deconstruction of gender stereotypes is essential to prevent discrimination, marginalization and gender-based violence. Despite the increasing attention to this issue, research in this field often focuses on explicitly sexist or hateful communication, leaving out all the cases where stereotypes are produced unconsciously or even with apparently positive intentions. Moreover, the identification and analysis of gender stereotypes is often a very subjective task, heavily influenced by the researcher’s background, beliefs and personal sensitivity. In this context GSI:detect, a dataset for gender stereotypes identification in Italian, has been annotated following a perspectivist approach that gives value to the different points of view of four annotators. It has been designed to address (i) the lack of resources focusing on naturally occurring and non-hateful language conveying implicit or ambiguous forms of gender stereotypes, and (ii) the scarcity of datasets that can capture multiple interpretations as well as the inherent variation and disagreement in human perception. Baseline experiments with several LLMs confirm the challenging nature and value of such a linguistic resource, revealing both apparent differences and limitations in performance among the evaluated models, and raising questions about the extent to which current LLMs are suitable for detection and classification tasks in this field. Content warning: Examples taken from the GSI:detect dataset may contain sensitive or potentially distressing content.

The paper proposes an investigation on the role of populist themes and rhetoric in an Italian Twitter corpus of hate speech against immigrants. The corpus had been annotated with four new layers of analysis: Nominal Utterances, that can be seen as consistent with populist rhetoric; In-out-group rhetoric, a very common populist strategy to polarize public opinion; Slogan-like nominal utterances, that may convey the call for severe illiberal policies against immigrants; News, to recognize the role of newspapers (headlines or reference to articles) in the Twitter political discourse on immigration featured by hate speech.