Offensive language detection systems often rely on majority-aggregated annotations, overlooking the diversity of perspectives that shape how different communities perceive harm. In this contribution, we introduce HurtLens, a perspectivist corpus of hurtful language leveraging four disaggregated datasets which are automatically enriched through HurtLex lemmas, a multilingual resource of offensive and derogatory terms. Using mixed-effects modeling, we investigate how annotators’ sociodemographic backgrounds, the presence of specific types of offensive language (through Hurtlex categories) and their interaction influence offensiveness ratings. Our analysis reveals that offensiveness ratings are influenced both by annotators’ sociodemographic characteristics (particularly when considering them in intersection) and by the presence of specific types of offensive language. Additionally, we identify significant interaction effects showing that different demographic groups vary in their sensitivity to texts containing particular types of offensive language.

Environmental issues are at the centre of a debate currently taking place across all communication channels. This paper provides an analysis of texts in which these issues are discussed, with the novelty of applying a methodology that enables the extraction and comparison of different narratives and points of view. The texts used in this study are the English Living Planet Reports published biennially by the WWF from 2014 to 2024. The methodology is based on the extraction of constructions – patterns collected in the English constructicon CASA – which allow us to identify differences in the presentation of the issues discussed in the analysed texts. Our results show that this methodology can be very helpful in the comparative analysis of texts to reveal different perspectives, for example, to observe diachronic variations.

Irony is a subjective and pragmatically complex phenomenon, often conveyed through rhetorical figures and interpreted differently across individuals. In this study, we adopt a perspectivist approach, accounting for the socio-demographic background of annotators, to investigate whether specific rhetorical strategies promote a shared perception of irony within demographic groups, and whether Large Language Models (LLMs) reflect specific perspectives. Focusing on the Italian subset of the perspectivist MultiPICo dataset, we manually annotate rhetorical figures in ironic replies using a linguistically grounded taxonomy. The annotation is carried out by expert annotators balanced by generation and gender, enabling us to analyze inter-group agreement and polarization. Our results show that some rhetorical figures lead to higher levels of agreement, suggesting that certain rhetorical strategies are more effective in promoting a shared perception of irony. We fine-tune multilingual LLMs for rhetorical figure classification, and evaluate whether their outputs align with different demographic perspectives. Results reveal that models show varying degrees of alignment with specific groups, reflecting potential perspectivist behavior in model predictions. These findings highlight the role of rhetorical figures in structuring irony perception and underscore the importance of socio-demographics in both annotation and model evaluation.

Emotions and language are strongly associated. In recent years, many resources have been created to investigate this association and automatically detect emotions from texts.Presenting ELIta (Emotion Lexicon for Italian), this study provides a new language resource for the analysis and detection of emotions in Italian texts. It describes the process of lexicon creation, including lexicon selection and annotation methodologies, and compares the collected data with existing resources. By offering a non-aggregated lexicon, ELIta fills a crucial gap and is applicable to various research and practical applications. Furthermore, the work utilises the lexicon by analysing the relationships between emotions and gender.

Abstract concepts, notwithstanding their lack of physical referents in real world, are grounded in sensorimotor experience. In fact, images depicting concrete entities may be associated to abstract concepts, both via direct and indirect grounding processes. However, what are the links connecting the concrete concepts represented by images and abstract ones is still unclear. To investigate these links, we conducted a preliminary study collecting word association data and image-abstract word pair ratings, to identify whether the associations between visual and verbal systems rely on the same conceptual mappings. The goal of this research is to understand to what extent linguistic associations could be confirmed with visual stimuli, in order to have a starting point for multimodal analysis of abstract and concrete concepts.

Metaphor is a widespread linguistic and cognitive phenomenon that is ruled by mechanisms which have received attention in the literature. Transformer Language Models such as BERT have brought improvements in metaphor-related tasks. However, they have been used only in application contexts, while their knowledge of the phenomenon has not been analyzed. To test what BERT knows about metaphors, we challenge it on a new dataset that we designed to test various aspects of this phenomenon such as variations in linguistic structure, variations in conventionality, the boundaries of the plausibility of a metaphor and the interpretations that we attribute to metaphoric expressions. Results bring out some tendencies that suggest that the model can reproduce some human intuitions about metaphors.