Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts

TL;DR

This study investigates how different amounts and types of textual annotations in charts affect viewer understanding and preferences, providing evidence-based guidelines for integrating text with visualizations.

Contribution

It offers the first experimental analysis of textual annotation effects on chart comprehension and preferences, informing design guidelines for combining text and visualizations.

Findings

Participants preferred heavily annotated charts over less annotated ones.

Semantic content influences the type of takeaways, with statistical and relational descriptions leading to specific insights.

Text placement affects the effectiveness of conveying different types of information.

Abstract

While visualizations are an effective way to represent insights about information, they rarely stand alone. When designing a visualization, text is often added to provide additional context and guidance for the reader. However, there is little experimental evidence to guide designers as to what is the right amount of text to show within a chart, what its qualitative properties should be, and where it should be placed. Prior work also shows variation in personal preferences for charts versus textual representations. In this paper, we explore several research questions about the relative value of textual components of visualizations. 302 participants ranked univariate line charts containing varying amounts of text, ranging from no text (except for the axes) to a written paragraph with no visuals. Participants also described what information they could take away from line charts containing text with varying semantic content. We find that heavily annotated charts were not penalized. In fact, participants preferred the charts with the largest number of textual annotations over charts with fewer annotations or text alone. We also find effects of semantic content. For instance, the text that describes statistical or relational components of a chart leads to more takeaways referring to statistics or relational comparisons than text describing elemental or encoded components. Finally, we find different effects for the semantic levels based on the placement of the text on the chart; some kinds of information are best placed in the title, while others should be placed closer to the data. We compile these results into four chart design guidelines and discuss future implications for the combination of text and charts.