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Explaining with Examples: Lessons Learned from Crowdsourced Introductory Description of Information Visualizations
Human-Computer Interaction · Submitted 23 Dec 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We find that introductions explaining visual encodings with concrete examples are the most effective.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Data visualizations have been increasingly used in oral presentations to communicate data patterns to the general public. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | Clear verbal introductions of visualizations to explain how to interpret the visually encoded information are essential to convey the takeaways and avoid misunderstandings. | background 0.99 | 0.05 | 0.04 | asserted 1.00 |
| s2 | We contribute a series of studies to investigate how to effectively introduce visualizations to the audience with varying degrees of visualization literacy. | contribution 1.00 | 0.86 | 0.03 | asserted 0.75 |
| s3 | We begin with understanding how people are introducing visualizations. | method 0.62 | 0.67 | 0.01 | unclear 0.60 |
| s4 | We crowdsource 110 introductions of visualizations and categorize them based on their content and structures. | method 0.98 | 0.97 | 0.01 | asserted 0.99 |
| s5 | From these crowdsourced introductions, we identify different introduction strategies and generate a set of introductions for evaluation. | method 0.94 | 0.96 | 0.02 | asserted 1.00 |
| s6 | We conduct experiments to systematically compare the effectiveness of different introduction strategies across four visualizations with 1,080 participants. | method 0.98 | 0.97 | 0.03 | asserted 0.87 |
| s7 | We find that introductions explaining visual encodings with concrete examples are the most effective. | result 1.00 | 0.97 | 0.93 | asserted 1.00 |
| s8 | 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. | interpretation 0.49 | 0.84 | 0.19 | asserted 0.94 |
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The paper
Original 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.
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