Social Story Frames: Contextual Reasoning about Narrative Intent and Reception
Joel Mire, Maria Antoniak, Steven R. Wilson, Zexin Ma, Achyutarama R. Ganti, Andrew Piper, Maarten Sap

TL;DR
SocialStoryFrames introduces a formalism and models for analyzing nuanced reader responses to stories, enabling large-scale study of storytelling practices across online communities.
Contribution
It presents a novel formalism and two models validated through human and expert annotations, linking narrative theory with computational analysis of social media stories.
Findings
Validated models through human surveys and expert annotations.
Characterized storytelling intents and practices across diverse social media communities.
Enabled large-scale analysis of narrative diversity and interdependence.
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…
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