Papers by Hannah Stone
Persistent Homology of Topic Networks for the Prediction of Reader Curiosity (2025.acl-long)
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Manuel D.s. Hopp, Vincent Labatut, Arthur Amalvy, Richard Dufour, Hannah Stone, Hayley K Jach, Kou Murayama
| Challenge: | Existing approaches to model reader engagement rely on surface-level characteristics and often fail to capture the broader semantic structure, narrative flow, and information gaps that stimulate curiosity. |
| Approach: | They propose a framework that quantifies semantic information gaps within a text's semantic structure by using BERTopic-inspired topic modeling and persistent homology to analyze the evolving topology of a dynamic semantic network derived from text segments. |
| Outcome: | The proposed method significantly improves curiosity prediction compared to baseline models (73% vs. 30% explained deviance) |