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.

Most NLP work on narrative detection has focused on prescriptive definitions of stories crafted by researchers, leaving open the questions: how do crowd workers perceive texts to be a story, and why? We investigate this by building StoryPerceptions, a dataset of 2,496 perceptions of storytelling in 502 social media texts from 255 crowd workers, including categorical labels along with free-text storytelling rationales, authorial intent, and more. We construct a fine-grained bottom-up taxonomy of crowd workers’ varied and nuanced perceptions of storytelling by open-coding their free-text rationales. Through comparative analyses at the label and code level, we illuminate patterns of disagreement among crowd workers and across other annotation contexts, including prescriptive labeling from researchers and LLM-based predictions. Notably, plot complexity, references to generalized or abstract actions, and holistic aesthetic judgments (such as a sense of cohesion) are especially important in disagreements. Our empirical findings broaden understanding of the types, relative importance, and contentiousness of features relevant to narrative detection, highlighting opportunities for future work on reader-contextualized models of narrative reception.

Story detection in online communities is a challenging task as stories are scattered across communities and interwoven with non-storytelling spans within a single text. We address this challenge by building and releasing the StorySeeker toolkit, including a richly annotated dataset of 502 Reddit posts and comments, a detailed codebook adapted to the social media context, and models to predict storytelling at the document and span levels. Our dataset is sampled from hundreds of popular English-language Reddit communities ranging across 33 topic categories, and it contains fine-grained expert annotations, including binary story labels, story spans, and event spans. We evaluate a range of detection methods using our data, and we identify the distinctive textual features of online storytelling, focusing on storytelling spans, which we introduce as a new task. We illuminate distributional characteristics of storytelling on a large community-centric social media platform, and we also conduct a case study on r/ChangeMyView, where storytelling is used as one of many persuasive strategies, illustrating that our data and models can be used for both inter- and intra-community research. Finally, we discuss implications of our tools and analyses for narratology and the study of online communities.