Pouring Your Heart Out: Investigating the Role of Figurative Language in Online Expressions of Empathy (2024)

Abstract: Empathy is a social mechanism used to support and strengthen emotional connection with others, including in online communities. However, little is currently known about the nature of these online expressions, nor the particular factors that may lead to their improved detection. In this work, we study the role of a specific and complex subcategory of linguistic phenomena, figurative language, in online expressions of empathy. Our extensive experiments reveal that incorporating features regarding the use of metaphor, idiom, and hyperbole into empathy detection models improves their performance, resulting in impressive maximum F1 scores of 0.942 and 0.809 for identifying posts without and with empathy, respectively.

## introduction empathy is a complex multidimensional communicative tool that involves the capacity to recognize and respond to the emotional experiences of individuals seeking help @xcite . it is employed in therapeutic interactions @xcite , including in the digital realm where millions seek solace and support within online communities @xcite . interest in online empathy has surged recently, leading to the creation of datasets in generalized domains like mental health @xcite . however, these broad investigations of empathy across diverse conditions make it difficult to draw nuanced conclusions regarding the nature of empathetic language. most investigations arising from large-scale initiatives (e.g., shared tasks organized by @xcite ) have predominantly relied on black-box approaches and focused on emotions and user demographics, hindering the development of a more comprehensive understanding of empathy in online environments and underscoring the need for further research. we conduct a two-pronged investigation to address these gaps. we (1) focus our study on domainspecific detection of expressed empathy @xcite related to the emotional and psychological effects associated with acne. by delving into this specific condition known to affect social and mental well-being @xcite , our work paves the way for more specialized empathy understanding and support mechanisms. we also (2) investigate the role of figurative language in expressions of empathy within this dataset, addressing an unexplored aspect of contemporary empathy detection research. we further demonstrate that integrating features describing the presence of metaphor, idiom, and hyperbole enhances empathy detection performance across feature-based and pretrained language model (plm) classifiers, offering a deeper understanding of empathetic expressions beyond emotional and demographic indicators. our key contributions include: we hope that the outcomes from this research inspire further work towards improved ai-driven support systems, including empathetic chatbots tailored to specific needs. additionally, our research may provide avenues for enhancing empathy training and feedback mechanisms for peer supporters in online communities, ultimately elevating the quality of support available to those seeking help. 519 2 background ## data the scarcity of domain-specific empathy datasets has constrained the depth to which it may be studied in specialized settings. our new dataset, ac-nempathize @xcite , focuses entirely on acne-related conversations. our objective in developing this dataset was to foster enhanced analy-sis of empathetic interactions in a well-motivated @xcite domainspecific setting. this dataset is publicly available. 2 here, we provide a concise overview of the dataset for context. we encourage readers to refer to @xcite for further details. ## figurative language in acnempathize the use of figurative language in empathy is unexplored, although @xcite found connections between affective properties and german idioms. we focus on three forms of figurative language-idioms, metaphors, and hyperboles-and systematically analyze how these figures of speech are used within acnempathize. ## empathy detection in acnempathize following our analysis of figurative language use in acnempathize, we investigated its role in empathy detection. we aimed to enhance performance by incorporating figurative language features more directly into our models. to ensure broad understanding of this phenomenon, we experimented with both feature-based and plm paradigms. for all experimental conditions, we utilized an 80:20 random split for training and testing data. ## conclusion in this paper, we investigate figurative language use in the new domain-specific empathy detection corpus, acnempathize. we find that incorporating figurative language features into domain-specific empathy detection models improves their performance, and we achieve an impressive maximum f 1 =0.942 and f 1 =0.809 when identifying posts with and without empathy, respectively. we release all models 10 and data 2 publicly to encourage follow-up research by others. in our systematic analysis of figurative language use in this dataset, we find confirmatory and intriguing associations between empathy, idiom, and metaphor. insights resulting from this study hold promise for improving peer-to-peer support and paving the way for the development of empathetic chatbots that cater to the concerns of different online communities. promising future directions include investigating the interplay between various 10 https://github.com/gyeongeunlee16/detect_acn empathize forms of figurative language and the implications of their combined use, and broadening our scope to include additional forms of figurative language.