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

arXiv: https://arxiv.org/abs/2405.17633 · HTML: https://mlsanity.com/paper/2405.17633/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 85 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 11 of 11)

- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [The emotional arcs of stories are dominated by six basic shapes](https://mlsanity.com/paper/1606.07772/) (2016)
- [Modeling Naive Psychology of Characters in Simple Commonsense Stories](https://mlsanity.com/paper/1805.06533/) (2018)
- [Modeling Empathy and Distress in Reaction to News Stories](https://mlsanity.com/paper/1808.10399/) (2018)
- [Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations](https://mlsanity.com/paper/2102.08368/) (2021)
- [Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers](https://mlsanity.com/paper/2403.01061/) (2024)
- [Large language models can segment narrative events similarly to humans](https://mlsanity.com/paper/2301.10297/) (2023)
- [Modeling Empathic Similarity in Personal Narratives](https://mlsanity.com/paper/2305.14246/) (2023)
- [EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences](https://mlsanity.com/paper/2405.15708/) (2024)
- [Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types](https://mlsanity.com/paper/2005.12762/) (2020)
- [Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding](https://mlsanity.com/paper/2310.18783/) (2023)

## Built on by (most cited first, 16 of 16)

- [A Survey of Personalization: From RAG to Agent](https://mlsanity.com/paper/2504.10147/) (2025)
- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [When Large Language Models are Reliable for Judging Empathic Communication](https://mlsanity.com/paper/2506.10150/) (2025)
- [Social World Models](https://mlsanity.com/paper/2509.00559/) (2025)
- [Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering](https://mlsanity.com/paper/2502.11491/) (2025)
- [A Simple "Try Again" Can Elicit Multi-Turn LLM Reasoning](https://mlsanity.com/paper/2507.14295/) (2025)
- [Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey](https://mlsanity.com/paper/2602.15851/) (2026)
- [Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication](https://mlsanity.com/paper/2505.21451/) (2025)
- [Literary Evidence Retrieval via Long-Context Language Models](https://mlsanity.com/paper/2506.03090/) (2025)
- [Are You There God? Lightweight Narrative Annotation of Christian Fiction with LMs](https://mlsanity.com/paper/2507.19756/) (2025)
- [Story Ribbons: Reimagining Storyline Visualizations with Large Language Models](https://mlsanity.com/paper/2508.06772/) (2025)
- [Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization](https://mlsanity.com/paper/2509.04646/) (2025)
- [Persuasion Index: A Theory-Guided Framework for Persuasion Analysis](https://mlsanity.com/paper/2606.14580/) (2026)
- [Semantic Primes as Explanans for Emotion in Large Language Models](https://mlsanity.com/paper/2607.18691/) (2026)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)
- [Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation](https://mlsanity.com/paper/2609.03407/) (2026)

## Abstract (arXiv)

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.