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.

翻译：阅读故事会引发丰富的解释性、情感性和评价性反应，例如对叙事意图的推断或对人物的评判。然而，读者反应的计算模型目前仍较为有限，阻碍了细致入微的分析。为填补这一空白，我们提出了SocialStoryFrames这一形式化框架，用于提炼关于读者反应的合理推断——包括感知到的作者意图、解释性与预测性推理、情感反应以及价值判断——该框架利用对话语境以及基于叙事理论、语言语用学和心理学的分类体系。我们开发了SSF-Generator和SSF-Classifier两个模型，分别通过人类调查（N=382名参与者）和专家标注进行验证。我们开展了试点分析，以展示该形式化框架在大规模叙事研究中的应用价值。具体而言，将我们的模型应用于SSF-Corpus（一个包含6,140个来自多元语境社交媒体故事的精选数据集），我们量化了叙事意图的频率与相互依存关系，并比较了不同社群间叙事实践（及其多样性）的异同。通过将细粒度、语境敏感的建模与读者反应的通用分类体系相结合，SocialStoryFrames为在线社群叙事研究开启了新的探索路径。