Contemporary environmental discourse focuses on effectively communicating ecological vulnerability to raise public awareness and encourage positive actions. Hence there is a need for studies to support accurate and adequate discourse production, both by humans and computers. Two main challenges need to be tackled. On the one hand, the language used to communicate about environment issues can be very complex for human and automatic analysis, there being few resources to train and test NLP tools. On the other hand, in the current international scenario, most texts are written in multiple languages or translated from a major to minor language, resulting in different meanings in different languages and cultural contexts. This paper presents a novel parallel corpus comprising the text of World Wide Fund (WWF) 2024 Annual Report in English and its translations into Italian and Brazilian Portuguese, and analyses their linguistic features.

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.

The increasing complexity of environmental discourse is directly proportional to the growing complexity of environmental debates present today in all communication media. While linguistic and communication studies have been pursued on this discourse, the development of computational linguistic tools and resources dedicated to support its analysis and interpretation is still very incipient. For one, no morphosyntactic resources specific to the environmental domain can be found on major platforms and repositories. This paper introduces TreEn, a multilingual treebank project in progress which compiles texts on environmental discourse produced in different conversational and communication contexts. In particular, it reports on the parallel component of the project and discusses issues faced during sentence-level alignment between original and translated texts, annotation of texts following UD guidelines, and labeling entities drawing on an ontology of environmental-related topics. This novel resource is expected to support environmental discourse analysis by providing morphological and syntactical data to enable cross-language and cross-cultural comparison based on the semantics of the entities annotated in the treebank.

The teaching laboratory we have created integrates methodologies to address the topic of hate speech on social media among students while fostering computational thinking and AI education for societal impact. We provide a foundational understanding of hate speech and introduce computational concepts using matrices, bag of words, and practical exercises in platforms like Colaboratory. Additionally, we emphasize the application of AI, particularly in NLP, to address real-world challenges. Through retrospective evaluation, we assess the efficacy of our approach, aiming to empower students as proactive contributors to societal betterment. With this paper we present an overview of the laboratory’s structure, the primary materials used, and insights gleaned from six editions conducted to the present date.

The paper introduces a novel corpus collected in a set of experiments in Italian schools, annotated for the presence of stereotypes, and related categories. It consists of comments written by teenage students in reaction to fabricated fake news, designed to elicit prejudiced responses, by featuring racial stereotypes. We make use of an annotation scheme which takes into account the implicit or explicit nature of different instances of stereotypes, alongside their forms of discredit. We also annotate the stance of the commenter towards the news article, using a schema inspired by rumor and fake news stance detection tasks. Through this rarely studied setting, we provide a preliminary exploration of the production of stereotypes in a more controlled context. Alongside this novel dataset, we provide both quantitative and qualitative analyses of these reactions, to validate the categories used in their annotation. Through this work, we hope to increase the diversity of available data in the study of the propagation and the dynamics of negative stereotypes.