Modeling Story Expectations to Understand Engagement: A Generative Framework Using LLMs
Hortense Fong, George Gui

TL;DR
This paper presents a novel framework using large language models to model audience expectations and beliefs about story developments, enhancing understanding of engagement behaviors in narrative media.
Contribution
It introduces a new generative approach leveraging LLMs to capture audience forward-looking beliefs, complementing existing content feature extraction methods.
Findings
Amplifies explanatory power of existing features by 31%
Different engagement types are driven by distinct content expectations
Framework provides insights into how beliefs influence engagement
Abstract
Understanding when and why consumers engage with stories is crucial for content creators and platforms. While existing theories suggest that audience beliefs of what is going to happen should play an important role in engagement decisions, empirical work has mostly focused on developing techniques to directly extract features from actual content, rather than capturing forward-looking beliefs, due to the lack of a principled way to model such beliefs in unstructured narrative data. To complement existing feature extraction techniques, this paper introduces a novel framework that leverages large language models to model audience forward-looking beliefs about how stories might unfold. Our method generates multiple potential continuations for each story and extracts features related to expectations, uncertainty, and surprise using established content analysis techniques. Applying our method…
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Taxonomy
TopicsBig Data and Business Intelligence
