# Semantic Interaction for Narrative Map Sensemaking: An Insight-based Evaluation

arXiv: https://arxiv.org/abs/2603.29651 · HTML: https://mlsanity.com/paper/2603.29651/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [A Survey on Event-based News Narrative Extraction](https://mlsanity.com/paper/2302.08351/) (2023)
- [Mixed Multi-Model Semantic Interaction for Graph-based Narrative Visualizations](https://mlsanity.com/paper/2302.06452/) (2023)
- [Design guidelines for narrative maps in sensemaking tasks](https://mlsanity.com/paper/2112.12205/) (2021)
- [Narrative Trails: A Method for Coherent Storyline Extraction via Maximum Capacity Path Optimization](https://mlsanity.com/paper/2503.15681/) (2025)
- [Interactive Narrative Analytics: Bridging Computational Narrative Extraction and Human Sensemaking](https://mlsanity.com/paper/2601.11459/) (2026)

## Built on by (most cited first, 2 of 2)

- [An Information-Geometric Justification for Composite Coherence in Event-Based Narrative Extraction](https://mlsanity.com/paper/2606.29118/) (2026)
- [Information Terra: A Narrative-Anchored Semantic-First Projection of Document Embeddings](https://mlsanity.com/paper/2606.30824/) (2026)

## Abstract (arXiv)

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 > 0.8), suggesting that the study was underpowered to detect them. Qualitative analysis identified two distinct SI approaches-corrective and additive-that enable analysts to impose quality judgments and organizational structure on extracted narratives. We also find that SI users achieved comparable exploration breadth with less parameter manipulation, suggesting that SI serves as an alternative pathway for model refinement. This work provides empirical evidence that map-based representations outperform timelines for narrative sensemaking, along with qualitative insights into how analysts use SI for narrative refinement.