构建故事理解框架，分析社交叙事中的意图与反应。
Social Story Frames: Contextual Reasoning about Narrative Intent and Reception
- 基于叙事理论设计可解释的读者反应建模框架
- 在6140条社交媒体故事上验证了意图多样性与社区差异
- 适合研究网络叙事、用户心理与社会传播的学者
阅读故事会引发丰富的解释、情感和评价反应，如对作者意图的推断或对角色的判断。然而，现有的计算模型在读者反应建模方面仍显不足，难以实现细致分析。为此，我们提出 SocialStoryFrames，一种基于对话上下文和叙事理论、语言语用学及心理学的分类体系，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。我们开发了两个模型：SSF-Generator 和 SSF-Classifier，分别通过包含382名参与者的问卷调查和专家标注进行验证。我们以自建的SSFCorpus数据集（6140条来自不同情境的社交媒体故事）为基础，开展初步分析，揭示叙事意图的频率分布及其相互依赖关系，并对比不同社群间的叙事实践及其多样性。该框架将细粒度、上下文敏感的建模与通用的读者反应分类相结合，为在线社群中讲故事的研究开辟新路径。
原文摘要 · Abstract (English)
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.
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