Quantifying the Information Flow of Long Narratives: A Case Study of Jane Austin’s Works

Tianyi Zhang
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Tianyi Zhang: School of International Studies, Zhejiang University, China

International Journal of Research and Innovation in Social Science, 2024, vol. 8, issue 8, 862-869

Abstract: This study employs digital humanities to analyze the information flow in Jane Austen’s classic literature using the GPT-2 XL model. Entropy, a measure of unpredictability, quantifies the narrative’s dynamic engagement with readers. By calculating the entropy of each sentence, the research reveals unique patterns of information gain across Austen’s novels, reflecting the ebb and flow of reader surprise. Peaks in entropy correspond to narrative climaxes, while declines indicate more predictable plot developments. The findings suggest that digital tools can offer fresh insights into literary analysis, highlighting the interplay between predictability and surprise in narrative structure. This exploratory approach to literature enriches traditional literary studies and opens new avenues for understanding reader engagement with classic texts.

Date: 2024
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