Social Story Frames: Contextual Reasoning about Narrative Intent and Reception

TL;DR

SocialStoryFrames addresses the challenge of modeling reader response to online storytelling in a context-rich, scalable way. It introduces a 10-dimensional SSF-Taxonomy and a two-stage pipeline (SSF-Generator for inference generation and SSF-Classifier for classification), with distillation-based training from GPT-4o and GPT-4.1 and extensive human validation. The framework is applied to SSF-Corpus (6,140 Reddit stories) to analyze narrative intents, author/reader dynamics, and cross-community diversity, revealing both convergences and distinct storytelling practices across communities. Together, SSF provides a principled, context-sensitive basis for large-scale, nuanced studies of online storytelling and its social functions in diverse online communities.

Abstract

Reading stories evokes rich interpretive, affective, and evaluative responses, such as inferences about narrative intent or judgments about characters. Yet, computational models of reader response are limited, preventing nuanced analyses. To address this gap, we introduce SocialStoryFrames, a formalism for distilling plausible inferences about reader response, such as perceived author intent, explanatory and predictive reasoning, affective responses, and value judgments, using conversational context and a taxonomy grounded in narrative theory, linguistic pragmatics, and psychology. We develop two models, SSF-Generator and SSF-Classifier, validated through human surveys (N=382 participants) and expert annotations, respectively. We conduct pilot analyses to showcase the utility of the formalism for studying storytelling at scale. Specifically, applying our models to SSF-Corpus, a curated dataset of 6,140 social media stories from diverse contexts, we characterize the frequency and interdependence of storytelling intents, and we compare and contrast narrative practices (and their diversity) across communities. By linking fine-grained, context-sensitive modeling with a generic taxonomy of reader responses, SocialStoryFrames enable new research into storytelling in online communities.

Figures (14)

• Figure 1: Storytelling evokes many forms of reader response. We introduce SocialStoryFrames, a formalism for reasoning about intent and reception of conversational stories on social media.
• Figure 2: We introduce SSF-Taxonomy, a taxonomy of reader response to informal storytelling on social media.
• Figure 3: Human Inference Plausibility Ratings. The rates of "very likely" or "somewhat likely" indicate that GPT-4o and our distilled SSF-Generator are effective at generating plausible inferences about reader response.
• Figure 4: Subreddit similarity rankings according to semantic similarity vs ssf-sim. Each point represents a subreddit pair, and off-diagonal points indicate metric disagreement.
• Figure 5: Author-centric and reader-centric lenses onto diversity of narrative practices across subreddits.