Understanding and Measuring the Effects of Graphical Dimensions on Viewers' Perceived Chart Credibility

Journalists and visualization designers include visualizations in their articles and storytelling tools to deliver their message effectively. But design decisions they make to represent information, such as the graphical dimensions they choose and the viewer's familiarity with the content can impact the viewer's perceived credibility of charts. Especially in a context where little is known about sources of online information. But there is little experimental evidence that designers can refer to make decisions. Hence, this work aims to study and measure the effects of graphical dimensions and people's familiarity with the content on viewers' perceived chart credibility. I plan to conduct a crowd-sourced study with three graphical dimensions conditions, which are traditional charts, text annotation, and infographics. Then I will test these conditions on two user groups, which are domain experts and non-experts. With these results, this work aims to provide chart guidelines for visual designers with experimental evidence.Comment: Published in PacificVis2023, Poste

My Model is Unfair, Do People Even Care? Visual Design Affects Trust and Perceived Bias in Machine Learning

Machine learning technology has become ubiquitous, but, unfortunately, often exhibits bias. As a consequence, disparate stakeholders need to interact with and make informed decisions about using machine learning models in everyday systems. Visualization technology can support stakeholders in understanding and evaluating trade-offs between, for example, accuracy and fairness of models. This paper aims to empirically answer "Can visualization design choices affect a stakeholder's perception of model bias, trust in a model, and willingness to adopt a model?" Through a series of controlled, crowd-sourced experiments with more than 1,500 participants, we identify a set of strategies people follow in deciding which models to trust. Our results show that men and women prioritize fairness and performance differently and that visual design choices significantly affect that prioritization. For example, women trust fairer models more often than men do, participants value fairness more when it is explained using text than as a bar chart, and being explicitly told a model is biased has a bigger impact than showing past biased performance. We test the generalizability of our results by comparing the effect of multiple textual and visual design choices and offer potential explanations of the cognitive mechanisms behind the difference in fairness perception and trust. Our research guides design considerations to support future work developing visualization systems for machine learning.Comment: 11 pages, 6 figures, to appear in IEEE Transactions of Visualization and Computer Graphics (Also in proceedings of IEEE VIS 2023

Why More Text is (Often) Better: Themes from Reader Preferences for Integration of Charts and Text

Given a choice between charts with minimal text and those with copious textual annotations, participants in a study (Stokes et al.) tended to prefer the charts with more text. This paper examines the qualitative responses of the participants' preferences for various stimuli integrating charts and text, including a text-only variant. A thematic analysis of these responses resulted in three main findings. First, readers commented most frequently on the presence or lack of context; they preferred to be informed, even when it sacrificed simplicity. Second, readers discussed the story-like component of the text-only variant and made little mention of narrative in relation to the chart variants. Finally, readers showed suspicion around possible misleading elements of the chart or text. These themes support findings from previous work on annotations, captions, and alternative text. We raise further questions regarding the combination of text and visual communication.Comment: 7 pages, 3 figures, accepted to the NLVIZ workshop at IEEE Transaction on Visualization and Graphics conferenc

Interactive applications and rhetorical devices for guiding parent-clinician communication through data visualizations

Effective communication between clinicians and parents of young children can decrease parents' anxiety and discomfort, help them handle bad news and uncertainty, and improve their adherence to proposed interventions. Parent-clinician communication further has the potential to facilitate collaboration and increase parents' empowerment. However when communication involves a discussion of the child's developmental delay or challenging behaviors, parents experience an emotional strain as they discuss hopes and fears, developmental concerns, and feelings of distress. As a consequence, communication challenges may emerge such as denial and the parent's resistance against the information that the clinician presents. In addition to the emotional strain, parents also experience a cognitive burden due to medical jargon or presentation of data that is inaccessible to them. In fact, in most health care settings, parents reported their expectation of more accessible information than is currently provided. In order to address these challenges, I present data visualization as a method of facilitating parent-clinician communication. This dissertation covers the cognitive perception and the practical application of data visualization in parent-clinician communication through: (1) rhetorical devices that are used to guide people's understanding of data visualizations, and (2) interactive applications I have built that explore the role of data visualizations in clinical communication. Through exploring cognitive and practical aspects of visualizations in communication, this dissertation makes three contributions. First, I showcase three interactive webtools that involve visualizations, and demonstrate that visualizations can facilitate family-clinician communication through overcoming 1) the emotional barriers by presenting children's behaviors to parents in an objective manner and 2) the cognitive barriers by acting as an anchor for conversation and presenting important developmental concepts or patterns that are hard to convey through words or text. Next, I identify features that make behavioral visualizations useful for various communication based tasks, such as displaying microbehaviors and providing a balanced representation of child-adult interaction, instead of solely focusing on the child behavior. Finally, I present visual and textual cues as rhetorical devices for shaping the message in the visualization and guiding the viewers through visualizations. These devices help reduce confusion and prevent miscommunication in visual-based communication as thus contribute to a more effective parent-clinician communication

Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts

While visualizations are an effective way to represent insights about information, they rarely stand alone. When designing a visualization, text is often added to provide additional context and guidance for the reader. However, there is little experimental evidence to guide designers as to what is the right amount of text to show within a chart, what its qualitative properties should be, and where it should be placed. Prior work also shows variation in personal preferences for charts versus textual representations. In this paper, we explore several research questions about the relative value of textual components of visualizations. 302 participants ranked univariate line charts containing varying amounts of text, ranging from no text (except for the axes) to a written paragraph with no visuals. Participants also described what information they could take away from line charts containing text with varying semantic content. We find that heavily annotated charts were not penalized. In fact, participants preferred the charts with the largest number of textual annotations over charts with fewer annotations or text alone. We also find effects of semantic content. For instance, the text that describes statistical or relational components of a chart leads to more takeaways referring to statistics or relational comparisons than text describing elemental or encoded components. Finally, we find different effects for the semantic levels based on the placement of the text on the chart; some kinds of information are best placed in the title, while others should be placed closer to the data. We compile these results into four chart design guidelines and discuss future implications for the combination of text and charts.Comment: 11 pages, 4 tables, 6 figures, accepted to IEEE Transaction on Visualization and Graphic

Same Data, Diverging Perspectives: The Power of Visualizations to Elicit Competing Interpretations

People routinely rely on data to make decisions, but the process can be riddled with biases. We show that patterns in data might be noticed first or more strongly, depending on how the data is visually represented or what the viewer finds salient. We also demonstrate that viewer interpretation of data is similar to that of 'ambiguous figures' such that two people looking at the same data can come to different decisions. In our studies, participants read visualizations depicting competitions between two entities, where one has a historical lead (A) but the other has been gaining momentum (B) and predicted a winner, across two chart types and three annotation approaches. They either saw the historical lead as salient and predicted that A would win, or saw the increasing momentum as salient and predicted B to win. These results suggest that decisions can be influenced by both how data are presented and what patterns people find visually salient

Polarizing Political Polls: How Visualization Design Choices Can Shape Public Opinion and Increase Political Polarization

While we typically focus on data visualization as a tool for facilitating cognitive tasks (e.g., learning facts, making decisions), we know relatively little about their second-order impacts on our opinions, attitudes, and values. For example, could design or framing choices interact with viewers' social cognitive biases in ways that promote political polarization? When reporting on U.S. attitudes toward public policies, it is popular to highlight the gap between Democrats and Republicans (e.g., with blue vs red connected dot plots). But these charts may encourage social-normative conformity, influencing viewers' attitudes to match the divided opinions shown in the visualization. We conducted three experiments examining visualization framing in the context of social conformity and polarization. Crowdworkers viewed charts showing simulated polling results for public policy proposals. We varied framing (aggregating data as non-partisan "All US Adults," or partisan "Democrat" and "Republican") and the visualized groups' support levels. Participants then reported their own support for each policy. We found that participants' attitudes biased significantly toward the group attitudes shown in the stimuli and this can increase inter-party attitude divergence. These results demonstrate that data visualizations can induce social conformity and accelerate political polarization. Choosing to visualize partisan divisions can divide us further

Communicative Information Visualizations: How to make data more understandable by the general public

Although data visualizations have been around for centuries and are encountered frequently by the general public, existing evidence suggests that a significant portion of people have difficulty understanding and interpreting them. It might not seem like a big problem when a reader misreads a weather map and finds themselves without an umbrella in a rainstorm, but for those who lack the means, experience, or ability to make sense of data, misreading a data visualization concerning public health and safety could be a matter of life and death. However, figuring out how to make visualizations truly usable for a diverse audience remains difficult. In my thesis, I examined three areas where altering current practices may help make data visualizations more understandable and impactful in the future