This paper investigates how different online communities perceive and discuss the environmental impact of AI through sentiment analysis and emotion detection. We analyze Reddit discussion from r/artificial and r/climatechange, using pre-trained models fine-tuned on social media data. Our analysis reveals distinct patterns in how these communities engage with AI’s environmental implications: the AI community demonstrates a shift from predominantly neutral and positive sentiment in posts to more balanced perspectives in comments, while the climate community maintains a more critical stance throughout discussions. The findings contribute to our understanding of how different communities conceptualize and respond to the environmental challenges of AI development.

This paper presents the Vulnerable Identities Recognition Corpus (VIRC), a novel resource designed to enhance hate speech analysis in Italian and Spanish news headlines. VIRC comprises 921 headlines, manually annotated for vulnerable identities, dangerous discourse, derogatory expressions, and entities. Our experiments reveal that large language models (LLMs) struggle significantly with the fine-grained identification of these elements, underscoring the complexity of detecting hate speech. VIRC stands out as the first resource of its kind in these languages, offering a richer annotation schema compared to existing corpora. The insights derived from VIRC can inform the development of sophisticated detection tools and the creation of policies and regulations to combat hate speech on social media, promoting a safer online environment. Future work will focus on expanding the corpus and refining annotation guidelines to further enhance its comprehensiveness and reliability.