Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality

TL;DR

This paper explores how textual narratives can enhance causality visualization, aiding users in understanding complex temporal event sequences through a new design space, user studies, and a system called CAUSEWORKS.

Contribution

It introduces a design space for textual narratives in causality visualization and demonstrates their effectiveness through user studies and the CAUSEWORKS system.

Findings

Textual narratives improve understanding of causality visualizations.

Participants recover causality information more accurately with narratives.

CAUSEWORKS effectively integrates automatic narratives for complex event analysis.

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.