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

arXiv: https://arxiv.org/abs/2112.12364 · HTML: https://mlsanity.com/paper/2112.12364/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2021.

## Built on by (most cited first, 8 of 8)

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- [A Taxonomy of Rater Disagreements: Surveying Challenges & Opportunities from the Perspective of Annotating Online Toxicity](https://mlsanity.com/paper/2311.04345/) (2023)
- [ScrollTimes: Tracing the Provenance of Paintings as a Window into History](https://mlsanity.com/paper/2306.08834/) (2023)
- [DRIVE-T: A Methodology for Discriminative and Representative Data Viz Item Selection for Literacy Construct and Assessment](https://mlsanity.com/paper/2508.04160/) (2025)
- [Prismatic: Interactive Multi-View Cluster Analysis of Concept Stocks](https://mlsanity.com/paper/2402.08978/) (2024)

## Abstract (arXiv)

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.