Communicating Visualizations without Visuals: Investigation of Visualization Alternative Text for People with Visual Impairments

Jung, Crescentia; Mehta, Shubham; Kulkarni, Atharva; Zhao, Yuhang; Kim, Yea-Seul

doi:10.1109/tvcg.2021.3114846

Cited by 60 publications

(41 citation statements)

References 49 publications

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“…Various accessible media publishers have established general guidelines for people to write natural language captions [3,4,14,15], but those guidelines often lack rationales and support from empirical experiments [5]. This critique motivates our empirical study to better understand how to write effective data captions.…”

Section: Related Workmentioning

confidence: 99%

“…Some methods of evaluating captions used in the past empirical research include semi-structured interviews [5], pre-constructed questions [6,12], and takeaways recall [1,7,9].…”

Section: Evaluating Captionsmentioning

confidence: 99%

“…Semi-structured interviews allow users to express their thoughts and preferences directly after reading captions [5]. While such methods can provide detailed and insightful qualitative results, it is challenging to generalize meaningful quantitative results at scale.…”

Section: Evaluating Captionsmentioning

confidence: 99%

See 2 more Smart Citations

How Do Captions Affect Visualization Reading?

Zhu¹,

Cheng²,

Wu³

2022

Preprint

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Captions help readers better understand visualizations. However, if the visualization is intended to communicate specific features, should the caption be statistical, and focus on specific values, or perceptual, and focus on general patterns? Prior work has shown that when captions mention visually salient features, readers tend to recall those features. Still, we lack explicit guidelines for how to compose the appropriate caption. Further, what if the author wishes to emphasize a less salient feature?In this paper, we study how the visual salience of the feature described in a caption, and the semantic level of the caption description, affect a reader’s takeaways from line charts. For each single- or multi-line chart, we generate 4 captions that 1) describe either the primary or secondary salient feature in a chart, and 2) describe the feature either at the statistical or perceptual levels. We then show participants random chart-caption pairs and record their takeaways. We find that the primary salient feature is more memorable for single-line charts when the caption is expressed at the statistical level; for primary and secondary features in multi-line charts, the perceptual level is more memorable. We also find that many readers will tend to recall y-axis numerical values when a caption is present.

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Section: Related Workmentioning

confidence: 99%

“…Some methods of evaluating captions used in the past empirical research include semi-structured interviews [5], pre-constructed questions [6,12], and takeaways recall [1,7,9].…”

Section: Evaluating Captionsmentioning

confidence: 99%

See 1 more Smart Citation

How Do Captions Affect Visualization Reading?

Zhu¹,

Cheng²,

Wu³

2022

Preprint

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show abstract

“…Finally, the strategies developed by scatterHatch and other recent software only address color-vision deficiencies and not other visual impairments such as double vision or complete blindness. In such cases, we need to incorporate accessibility features such as screen-reader friendly alternate texts 16 which describe the graphical elements of the visualizations.…”

Section: Discussionmentioning

confidence: 99%

scatterHatch: an R/Bioconductor package for generating colorblind-friendly scatter plots for single-cell data

Guha

Fertig

Deshpande

2021

Preprint

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Summary: Color is often used as a primary differentiating factor in visualization of single-cell and multi-omics analyses. However, color-based visualizations are extremely limiting and require additional considerations to account for the wide range of color perceptions in the population. The scatterHatch package provides software for accessible single-cell visualizations that use patterns in conjunction with colors to amplify the distinction between different cell types, states, and groups. Availability: scatterHatch is available on Github at https://github.com/FertigLab/scatterHatch. Supplementary information: Supplementary figures are provided in the attached document.

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“…Schaadhardt et al [62] studied how blind users experience and work with digital artboards with colored objects through descriptions provided by screen readers. Jung et al [34] found that the usefulness of alt-text descriptions depended on their ability to help blind and low-vision participants "visualize" the information delivered. While prior studies confrm and seek to support PVI's use of mental images, the mental images are typically assumed.…”

Section: The Use Of Mental Images By Pvi To Engage With Visual Inform...mentioning

confidence: 99%

“So What? What's That to Do With Me?” Expectations of People With Visual Impairments for Image Descriptions in Their Personal Photo Activities

Jung

Steinberger

Kim³

et al. 2022

Designing Interactive Systems Conference

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People with visual impairments (PVI) access photos through image descriptions. Thus far, research has studied what PVI expect in these descriptions mostly regarding functional purposes (e.g., identifying an object) and when engaging with online, publicly available images. Extending this research, we interviewed 30 PVI to understand their expectations for image descriptions when viewing, taking, searching, and reminiscing with personal photos on their own devices. We show how their expectations varied across photo activities and often went well beyond identifying objects in photos. Based on our fndings, we propose design opportunities for generating and providing image descriptions for personal photo use by PVI. The design opportunities for PVI also point to novel support for the sighted for using image descriptions to enrich their experience of photos. CCS CONCEPTS• Human-centered computing → Accessibility.

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Communicating Visualizations without Visuals: Investigation of Visualization Alternative Text for People with Visual Impairments

Cited by 60 publications

References 49 publications

How Do Captions Affect Visualization Reading?

How Do Captions Affect Visualization Reading?

scatterHatch: an R/Bioconductor package for generating colorblind-friendly scatter plots for single-cell data

“So What? What's That to Do With Me?” Expectations of People With Visual Impairments for Image Descriptions in Their Personal Photo Activities

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