Towards a Perspectivist Understanding of Irony through Rhetorical Figures (2025)

Abstract: 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.

## introduction irony is a complex communicative phenomenon in which the intended meaning diverges from the literal interpretation of an utterance @xcite . it often relies on pragmatic inference and contextual cues, making it a challenging target for computational modeling. beyond its linguistic complexity, irony is also deeply subjective: its perception * equal contribution. linguistic studies distinguish several categories of irony, including hyperbole, exaggeration, and changes in register, conveyed through rhetorical figures @xcite . these rhetorical figures can be seen as markers of different categories of irony, each relying on distinct communicative cues @xcite . recognizing such strategies may therefore aid in detecting irony and understanding how it is perceived across individuals. at the same time, the subjectivity inherent in irony interpretation poses a challenge: what one person may find clearly ironic, another may interpret literally or fail to recognize altogether. this perspectivist dimension @xcite highlights the subjective variability in irony perception, posing challenges for both annotation and computational modeling. in this paper, we study irony not as a uniform phenomenon, but as a set of rhetorical categories that shape its interpretation. specifically, we investigate whether certain rhetorical figures promote a shared perception of irony categories among individuals who share socio-demographic traits-and whether such alignment can also be observed in the behavior of large language models (llms). indeed, llms have emerged as powerful tools for natural language understanding and generation. their ability to capture subtle patterns in language makes them promising candidates for modeling complex pragmatic phenomena such as irony @xcite . yet, llms are not neutral observers: their outputs reflect the data they were trained on, which may embed implicit cultural backgrounds, social perspectives, or biases @xcite . when applied to subjective phenomena like irony, this raises the question of whether llms themselves adopt specific perspec-tives in how they interpret rhetorical and ironic content @xcite . to this end, in the first part of the paper, we focus on the italian subset of the perspectivist mul-tipico dataset @xcite , which contains short social media conversations annotated for irony (ironic versus not-ironic) by a diverse pool of annotators. so, we augment the multipico annotation by manually annotating the rhetorical figures, adopting the taxonomy proposed by @xcite , into the replies that were labeled as ironic by majority vote in the original campaign. this process is carried out by annotators grouped by generation and gender, allowing us to examine patterns of agreement both within and across demographic groups. in the second part of the study, we first train llms to automatically classify rhetorical figures in ironic replies. in order to improve classification performance, we fine-tune the models on twittirò-ud @xcite , a corpus of ironic italian tweets annotated with rhetorical figures. we then examine whether the predictions made by the models reflect the annotation patterns of particular demographic groups-thus highlighting potential perspectivist biases in how llms handle complex pragmatic phenomena like irony. our study is guided by the following research questions (rqs): the remainder of the paper is structured as follows. section 2 reviews the literature on irony, rhetorical figures, and perspectivist annotation. section 3 introduces the multipico dataset. section 4 outlines our experimental design, followed by the manual annotation campaign and result analysis in section 5. in section 6, we present the automatic classification experiments with llms. we conclude with a summary of findings in section 7 and a discussion of limitations in section 8. ## related works recent work in nlp has increasingly emphasized the importance of taking annotators' perspectives into account when dealing with subjective linguistic phenomena such as irony or hate speech. instead of treating disagreement as a flaw to be minimized, the perspectivist approach @xcite considers it meaningful variation that reflects different ways of interpreting language. to support this view, several studies have proposed modeling annotations at the level of individuals @xcite or groups defined by shared beliefs or demographic traits @xcite . this line of research relies on disaggregated datasets, where annotations are linked to metadata such as age, gender, ideology, or cultural background @xcite . these datasets allow researchers to investigate how socio-demographic traits influence linguistic judgments, and to build models that better capture the diversity of interpretations @xcite . incorporating this information has been shown to improve not only fairness, but also classification performance. in the domain of irony detection, several studies have started to explore the relationship between perspectivism and the perception of irony @xcite , revealing, for instance, that irony can be more polarizing depending on the annotators' generation @xcite . in line with this direction, the present work aims to further investigate the perspectivist nature of irony by considering it as a phenomenon that can be classified into rhetorical categories @xcite . specifically, we propose a study that seeks to explain and analyze the role of annotators' perspectives in the perception and classification of irony through rhetorical figures. ## multipico multipico @xcite is a multilingual dataset of short social media conversations, each consisting of a post and its reply, annotated to indicate whether the reply is ironic in response to the post. it contains a total of 18,778 post-reply pairs collected from reddit (8,956) and twitter (9,822), spanning nine languages. the annotations were obtained through crowdsourcing from 506 individuals with diverse demographic profiles, resulting in 94,342 labels-an average of 5.02 annotations per post-reply pair. each label is enriched with demographic metadata, including gender, age, ethnicity, student status, and employment. in the italian subset, 24 annotators provided 4,790 labels across 1,000 conversations. foot_0 among them, 11 were female and 13 male. with respect to age groups, 11 annotators belonged to gen z (born between 1997 and 2012), 12 to gen y or @xcite , and 1 to gen x @xcite . ## methodology the first step of our methodology consists in the manual annotation of the italian subset of mul-tipico by linguistically trained experts with specific knowledge of rhetorical figures. we adopt the taxonomy proposed by @xcite , which classifies irony into eight categories. seven of these are grounded in rhetorical structures, while the eighth-other -serves as an umbrella category encompassing situational irony and humor @xcite . the seven rhetorical categories are as follows: all annotators belong to the same demographic groups considered in the original multipico annotation campaign. for this study, we focus on two dimensions: gender and generation. a subset of 200 ironic italian post-reply pairs was annotated by six individuals-three male and three female-balanced across generations: two from gen x, two from gen y, and two from gen z. we then analyze whether these groups show consistent patterns in the identification of rhetorical figures for ironic texts, both within and across demographic groups, in order to address our first research question. in the second phase of the study, we fine-tune various llms on rhetorical figure classification. we then evaluate their capability to classify rhetorical figures in ironic post-reply pairs from multi-pico. finally, we investigate whether these llms exhibit specific perspectives in their classification outputs, analyzing potential alignment with human demographic groups. ## multipico annotation in this section, we describe the annotation of the italian subset of multipico using the taxonomy proposed by @xcite , which was specifically developed for the analysis of ironic texts. we focus exclusively on post-reply pairs annotated as ironic in multipico, selected through a majority vote strategy. this yields a total of 278 ironic post-reply pairs. the annotation was performed by six volunteer native italian speakers, all with a strong academic background in linguistics, on 200 out of the 278 ironic post-reply pairs. the annotation process follows these steps: ## rhetorical figure classification and perspective alignment in llms in this section, we explore whether llms reflect specific perspectives when classifying rhetorical figures in ironic texts. as a first step, we fine-tuned a set of multilingual llms on the twittirò-ud dataset, aiming to enhance their performance in the classification of rhetorical figures within ironic language. indeed, while the twittirò-ud dataset serves to fine-tune and evaluate the llms' classification abilities, the multipico data instead allow us to assess whether model predictions align more closely with specific demographic perspectives. twittirò-ud twittirò-ud is a corpus of ironic italian tweets annotated with rhetorical figures and linguistic information following the universal dependencies (ud) framework. 4 it contains 1,424 tweets and over 28,000 tokens, originally collected for the fine-grained annotation of irony. each tweet is labeled with the rhetorical figure used to convey irony, based on the taxonomy proposed by @xcite . ## conclusions in this paper we investigated irony as a multifaceted phenomenon, structured by different rhetorical figures that guide its interpretation. by focusing on the italian subset of the perspectivist multipico dataset, we conducted a manual annotation campaign in which expert annotators labeled rhetorical figures in ironic replies. the annotators were balanced across gender and generation, allowing us to explore patterns of agreement both within and across demographic groups. our findings show that only some rhetorical figures-such as rhetorical question, hyperbole, and analogy-promote a more shared perception of irony categories. others yielded lower agreement, highlighting the subjective nature of this task. despite the linguistic expertise of the annotators and the use of detailed guidelines, the overall agreement remained modest, supporting the perspectivist view that irony interpretation is influenced by sociodemographic background. we then trained and evaluated llms on rhetorical figure classification. while fine-tuned models outperformed baselines, their predictions showed variation depending on which group's annotations were used as gold labels. in particular, different models aligned more closely with different demographic perspectives-suggesting that llms may replicate specific patterns observed in human annotation. these results emphasize the importance of incorporating socio-demographic information when modeling complex pragmatic phenomena such as irony, both to improve classification performance and to better account for variation in human interpretation.