Paper Info
Title
Once Upon A Time In Visualization: Understanding The Use Of Textual Narratives For Causality
Abstract
Causality visualization can help people understand temporal chains of events, such as messages sent in a distributed system, cause and effect in a historical conflict, or the interplay between political actors over time. However, as the scale and complexity of these event sequences grows, even these visualizations can become overwhelming to use. In this paper, we propose the use of textual narratives as a data-driven storytelling method to augment causality visualization. We first propose a design space for how textual narratives can be used to describe causal data. We then present results from a crowdsourced user study where participants were asked to recover causality information from two causality visualizations-causal graphs and Hasse diagrams-with and without an associated textual narrative. Finally, we describe Causeworks, a causality visualization system for understanding how specific interventions influence a causal model. The system incorporates an automatic textual narrative mechanism based on our design space. We validate Causeworks through interviews with experts who used the system for understanding complex events.
Year
DOI
Venue
2021
10.1109/TVCG.2020.3030358
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Keywords
DocType
Volume
Data visualization, Visualization, Natural languages, Pipelines, Task analysis, Correlation, Tools, Causality visualization, natural language generation, data-driven storytelling, temporal data, quantitative studies
Journal
27
Issue
ISSN
Citations 
2
1077-2626
0
PageRank 
References 
Authors
0.34
22
7
Name
Order
Citations
PageRank
Arjun Choudhry100.68
Mandar Sharma200.34
Pramod Chundury311.37
Thomas Kapler421.73
Derek W. S. Gray500.68
Naren Ramakrishnan61913176.25
Niklas Elmqvist7206598.35