arXiv subjects

Crystal Lee

Publications and source records attributed to Crystal Lee.

5 recordsLinked to original sources

arXiv2022

Rich Screen Reader Experiences for Accessible Data Visualization

Current web accessibility guidelines ask visualization designers to support screen readers via basic non-visual alternatives like textual descriptions and access to raw data tables. But charts do more than summarize data or reproduce tables; they afford interactive data exploration at varying levels of granularity -- from fine-grained datum-by-datum reading to skimming and surfacing high-level trends. In response to the lack of comparable non-visual affordances, we present a set of rich screen reader experiences for accessible data visualization and exploration. Through an iterative co-design process, we identify three key design dimensions for expressive screen reader accessibility: structure, or how chart entities should be organized for a screen reader to traverse; navigation, or the structural, spatial, and targeted operations a user might perform to step through the structure; and, description, or the semantic content, composition, and verbosity of the screen reader's narration. We operationalize these dimensions to prototype screen-reader-accessible visualizations that cover a diverse range of chart types and combinations of our design dimensions. We evaluate a subset of these prototypes in a mixed-methods study with 13 blind and low vision readers. Our findings demonstrate that these designs help users conceptualize data spatially, selectively attend to data of interest at different levels of granularity, and experience control and agency over their data analysis process. An accessible HTML version of this paper is available at: http://vis.csail.mit.edu/pubs/rich-screen-reader-vis-experiences.

cs.HC↗

arXiv2021

Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online

Controversial understandings of the coronavirus pandemic have turned data visualizations into a battleground. Defying public health officials, coronavirus skeptics on US social media spent much of 2020 creating data visualizations showing that the government's pandemic response was excessive and that the crisis was over. This paper investigates how pandemic visualizations circulated on social media, and shows that people who mistrust the scientific establishment often deploy the same rhetorics of data-driven decision-making used by experts, but to advocate for radical policy changes. Using a quantitative analysis of how visualizations spread on Twitter and an ethnographic approach to analyzing conversations about COVID data on Facebook, we document an epistemological gap that leads pro- and anti-mask groups to draw drastically different inferences from similar data. Ultimately, we argue that the deployment of COVID data visualizations reflect a deeper sociopolitical rift regarding the place of science in public life.

cs.HC↗

arXiv2019

Sociotechnical Considerations for Accessible Visualization Design

Accessibility--the process of designing for people with disabilities (PWD)--is an important but under-explored challenge in the visualization research community. Without careful attention, and if PWD are not included as equal participants throughout the process, there is a danger of perpetuating a vision-first approach to accessible design that marginalizes the lived experience of disability (e.g., by creating overly simplistic "sensory translations" that map visual to non-visual modalities in a one-to-one fashion). In this paper, we present a set of sociotechnical considerations for research in accessible visualization design, drawing on literature in disability studies, tactile information systems, and participatory methods. We identify that using state-of-the-art technologies may introduce more barriers to access than they remove, and that expectations of research novelty may not produce outcomes well-aligned with the needs of disability communities. Instead, to promote a more inclusive design process, we emphasize the importance of clearly communicating goals, following existing accessibility guidelines, and treating PWD as equal participants who are compensated for their specialized skills. To illustrate how these considerations can be applied in practice, we discuss a case study of an inclusive design workshop held in collaboration with the Perkins School for the Blind.

cs.CY↗

arXiv2016

Photographic home styles in Congress: a computer vision approach

While members of Congress now routinely communicate with constituents using images on a variety of internet platforms, little is known about how images are used as a means of strategic political communication. This is due primarily to computational limitations which have prevented large-scale, systematic analyses of image features. New developments in computer vision, however, are bringing the systematic study of images within reach. Here, we develop a framework for understanding visual political communication by extending Fenno's analysis of home style (Fenno 1978) to images and introduce "photographic" home styles. Using approximately 192,000 photographs collected from MCs Facebook profiles, we build machine learning software with convolutional neural networks and conduct an image manipulation experiment to explore how the race of people that MCs pose with shape photographic home styles. We find evidence that electoral pressures shape photographic home styles and demonstrate that Democratic and Republican members of Congress use images in very different ways.

cs.SI↗

arXiv2015

A Century of Portraits: A Visual Historical Record of American High School Yearbooks

Imagery offers a rich description of our world and communicates a volume and type of information that cannot be captured by text alone. Since the invention of the camera, an ever-increasing number of photographs document our "visual culture" complementing historical texts. But currently, this treasure trove of knowledge can only be analyzed manually by historians, and only at small scale. In this paper we perform automated analysis on a large-scale historical image dataset. Our main contributions are: 1) A publicly-available dataset of 168,055 (37,921 frontal-facing) American high school yearbook portraits. 2) Weakly-supervised data-driven techniques to discover historical visual trends in fashion and identify date-specific visual patterns. 3) A classifier to predict when a portrait was taken, with median error of 4 years for women and 6 for men. 4) A new method for discovering and displaying the visual elements used by the CNN-based date-prediction model to date portraits, finding that they correspond to the tell-tale fashions of each era. Project page can be found at: http://people.eecs.berkeley.edu/~shiry/projects/yearbooks/yearbooks.html .

cs.CV↗