Explaining with Examples: Lessons Learned from Crowdsourced Introductory Description of Information Visualizations

TL;DR

This paper investigates how verbal introductions of data visualizations can be optimized for clarity, finding that concrete examples significantly improve audience understanding across various visualization types.

Contribution

It introduces a systematic approach to categorize and evaluate visualization introductions, highlighting effective strategies like using concrete examples for better comprehension.

Findings

Concrete example explanations are most effective.

Crowdsourced data helped identify diverse introduction strategies.

Experimental results show improved understanding with example-based introductions.

Abstract

Data visualizations have been increasingly used in oral presentations to communicate data patterns to the general public. Clear verbal introductions of visualizations to explain how to interpret the visually encoded information are essential to convey the takeaways and avoid misunderstandings. We contribute a series of studies to investigate how to effectively introduce visualizations to the audience with varying degrees of visualization literacy. We begin with understanding how people are introducing visualizations. We crowdsource 110 introductions of visualizations and categorize them based on their content and structures. From these crowdsourced introductions, we identify different introduction strategies and generate a set of introductions for evaluation. We conduct experiments to systematically compare the effectiveness of different introduction strategies across four visualizations with 1,080 participants. We find that introductions explaining visual encodings with concrete examples are the most effective. Our study provides both qualitative and quantitative insights into how to construct effective verbal introductions of visualizations in presentations, inspiring further research in data storytelling.