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.

翻译：语义交互（SI）使分析人员能够通过直接操作可视化将认知过程融入AI模型。尽管面向叙事提取的SI框架已有提出，但其有效性的实证评估仍有限。本文开展了一项用户研究，评估SI在叙事地图意义构建中的作用，涉及33名参与者，在三种条件下进行实验：时间线基线、基本叙事地图以及具备SI功能的交互式叙事地图。结果表明，基于地图的原型比时间线基线产生了更多洞察，其中具备SI功能的条件达到统计显著性，而基本地图条件呈现相同趋势。SI功能条件显示出最高平均性能；地图条件间的差异虽未达到统计显著性，但效应量较大（d>0.8），表明研究统计效力不足以检测到差异。定性分析识别出两种不同的SI方法——校正型与补充型——使分析人员能够对提取的叙事施加质量判断与组织结构。我们还发现，SI用户以更少的参数操作实现了相当的探索广度，表明SI可作为模型优化的替代路径。本研究提供了地图表征优于时间线叙事意义构建的实证证据，并揭示了分析人员如何利用SI进行叙事优化的定性洞见。