A Survey on Annotations in Information Visualization: Empirical Insights, Applications, and Challenges
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Abstract (arXiv)
We present a comprehensive survey on the use of annotations in information visualizations, highlighting their crucial role in improving audience understanding and engagement with visual data. Our investigation encompasses empirical studies on annotations, showcasing their impact on user engagement, interaction, comprehension, and memorability across various contexts. We also study the existing tools and techniques for creating annotations and their diverse applications, enhancing the understanding of both practical and theoretical aspects of annotations in data visualization. Additionally, we identify existing research gaps and propose potential future research directions, making our survey a valuable resource for researchers, visualization designers, and practitioners by providing a thorough understanding of the application of annotations in visualization.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Oct 2024.
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Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025CHART-6: Human-Centered Evaluation of Data Visualization Understanding in Vision-Language Modelscited by 5
- 2026AnnoBench: A Benchmark for Visualization Annotation Generationcited by 1
- 2026ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generationcited by 0
- 2026Where LLMs Fail with Visualization DSLscited by 0
- 2026Where LLMs Fail with Visualization DSLs1 Oct 2026
- 2026ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation4 Aug 2026
- 2026AnnoBench: A Benchmark for Visualization Annotation Generation28 Jul 2026
- 2025CHART-6: Human-Centered Evaluation of Data Visualization Understanding in Vision-Language Models22 May 2025