HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs

• Main Authors: Shen, Jocelyn, Mire, Joel, Park, Hae Won, Breazeal, Cynthia, Sap, Maarten
• Format: Journal Article
• Language: English
• Published: 27-05-2024
• Subjects: Computer Science - Computation and Language
• DOI: 10.48550/arxiv.2405.17633

Empathy serves as a cornerstone in enabling prosocial behaviors, and can be
evoked through sharing of personal experiences in stories. While empathy is
influenced by narrative content, intuitively, people respond to the way a story
is told as well, through narrative style. Yet the relationship between empathy
and narrative style is not fully understood. In this work, we empirically
examine and quantify this relationship between style and empathy using LLMs and
large-scale crowdsourcing studies. We introduce a novel, theory-based taxonomy,
HEART (Human Empathy and Narrative Taxonomy) that delineates elements of
narrative style that can lead to empathy with the narrator of a story. We
establish the performance of LLMs in extracting narrative elements from HEART,
showing that prompting with our taxonomy leads to reasonable, human-level
annotations beyond what prior lexicon-based methods can do. To show empirical
use of our taxonomy, we collect a dataset of empathy judgments of stories via a
large-scale crowdsourcing study with N=2,624 participants. We show that
narrative elements extracted via LLMs, in particular, vividness of emotions and
plot volume, can elucidate the pathways by which narrative style cultivates
empathy towards personal stories. Our work suggests that such models can be
used for narrative analyses that lead to human-centered social and behavioral
insights.