A Directed Graph Model and Experimental Framework for Design and Study of Time-Dependent Text Visualisation
Songhai Fan, Simon Angus, Tim Dwyer, Ying Yang, Sarah Goodwin, Helen Purchase

TL;DR
This paper introduces a directed graph model for time-dependent text visualization, develops a synthetic text generation framework using LLMs, and evaluates user interpretability challenges through a controlled study.
Contribution
It presents a formal model for visualizing evolving text relationships and a synthetic data methodology to test user understanding of such visualizations.
Findings
Users find it challenging to identify predefined motifs in visualizations.
Qualitative analysis reveals diverse user rationales and interpretation divergences.
Synthetic data generation with LLMs introduces complexities affecting study control.
Abstract
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…
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Taxonomy
TopicsData Visualization and Analytics · Computational and Text Analysis Methods · Digital Humanities and Scholarship
