Semantic Interaction for Narrative Map Sensemaking: An Insight-based Evaluation
Brian Felipe Keith-Norambuena, Fausto German, Eric Krokos, Sarah Joseph, Chris North

TL;DR
This study empirically evaluates semantic interaction in narrative map sensemaking, demonstrating its effectiveness over timelines and revealing two distinct SI approaches through a user study with 33 participants.
Contribution
It provides the first empirical evidence that semantic interaction enhances narrative map sensemaking and introduces qualitative insights into SI usage strategies.
Findings
Map-based prototypes outperform timelines in insight generation.
SI-enabled condition shows the highest mean performance, with large effect sizes.
Two SI approaches, corrective and additive, were identified.
Abstract
Semantic interaction (SI) enables analysts to incorporate their cognitive processes into AI models through direct manipulation of visualizations. While SI frameworks for narrative extraction have been proposed, empirical evaluations of their effectiveness remain limited. This paper presents a user study that evaluates SI for narrative map sensemaking, involving 33 participants under three conditions: a timeline baseline, a basic narrative map, and an interactive narrative map with SI capabilities. The results show that the map-based prototypes yielded more insights than the timeline baseline, with the SI-enabled condition reaching statistical significance and the basic map condition trending in the same direction. The SI-enabled condition showed the highest mean performance; differences between the map conditions were not statistically significant but showed large effect sizes (d >…
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