用图模型和实验框架研究时间文本可视化中用户理解难题。
A Directed Graph Model and Experimental Framework for Design and Study of Time-Dependent Text Visualisation
- 构建基于有向图的时序文本可视化抽象模型，提炼出文本关联的典型模式。
- 通过LLM生成30组结构化虚构文本，验证用户识别模式的能力有限。
- 发现用户解释差异大，提示可视化需个性化适配而非统一设计。
数字新闻、社交媒体等文本源的指数级增长使人们难以跟上全球事件的快速演变叙事。多种可视化技术被提出，通过展现文本间随时间演化的主题与关系来辅助理解。然而，这些可视化是否有效，依赖于用户能否轻松解读其网络结构中的关系。为此，本文基于有向图结构建立时序文本可视化的抽象模型，从中提炼出文本跨时间连接的典型模式（motifs）。我们开发了一种受控的合成文本生成方法，利用现代大语言模型生成符合各模式的虚构但结构化文本集。在一项包含30名参与者的研究中，要求他们识别给定合成文章集所对应的预设模式。结果显示，用户准确恢复预设模式难度很高。定性分析揭示了用户在偏离预期解释时表现出丰富多样的推理逻辑。进一步分析还指出，使用LLM生成合成数据时存在意外复杂性，部分情况下削弱了实验控制力。此外，对个体决策过程的考察暗示未来文本叙事可视化应摆脱‘一刀切’模式，转向更适应具体用户的动态适配设计。
原文摘要 · Abstract (English)
Exponential growth in the quantity of digital news, social media, and other textual sources makes it difficult for humans to keep up with rapidly evolving narratives about world events. Various visualisation techniques have been touted to help people to understand such discourse by exposing relationships between texts (such as news articles) as topics and themes evolve over time. Arguably, the understandability of such visualisations hinges on the assumption that people will be able to easily interpret the relationships in such visual network structures. To test this assumption, we begin by defining an abstract model of time-dependent text visualisation based on directed graph structures. From this model we distill motifs that capture the set of possible ways that texts can be linked across changes in time. We also develop a controlled synthetic text generation methodology that leverages the power of modern LLMs to create fictional, yet structured sets of time-dependent texts that fit each of our patterns. Therefore, we create a clean user study environment (n=30) for participants to identify patterns that best represent a given set of synthetic articles. We find that it is a challenging task for the user to identify and recover the predefined motif. We analyse qualitative data to map an unexpectedly rich variety of user rationales when divergences from expected interpretation occur. A deeper analysis also points to unexpected complexities inherent in the formation of synthetic datasets with LLMs that undermine the study control in some cases. Furthermore, analysis of individual decision-making in our study hints at a future where text discourse visualisation may need to dispense with a one-size-fits-all approach and, instead, should be more adaptable to the specific user who is exploring the visualisation in front of them.
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