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.