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

Leni Yang; Cindy Xiong; Jason K. Wong; Aoyu Wu; Huamin Qu

View presentation:2022-10-21T14:36:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
The figure shows a design space concluded from introductions of data charts by 110 participants.

Prerecorded Talk

The live footage of the talk, including the Q&A, can be viewed on the session page, Natural Language Interaction.

Fast forward

narrative visualization, oral presentation, introduction


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.