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1. Vistrust: a Multidimensional Framework and Empirical Study of Trust in Data Visualizations

   https://doi.org/10.1109/TVCG.2023.3326579  

   Elhamdadi, Hamza; Stefkovics, Adam; Beyer, Johanna; Moerth, Eric; Pfister, Hanspeter; Bearfield, Cindy Xiong; Nobre, Carolina (January 2024, IEEE Transactions on Visualization and Computer Graphics)

   Trust is an essential aspect of data visualization, as it plays a crucial role in the interpretation and decision-making processes of users. While research in social sciences outlines the multi-dimensional factors that can play a role in trust formation, most data visualization trust researchers employ a single-item scale to measure trust. We address this gap by proposing a comprehensive, multidimensional conceptualization and operationalization of trust in visualization. We do this by applying general theories of trust from social sciences, as well as synthesizing and extending earlier work and factors identified by studies in the visualization field. We apply a two-dimensional approach to trust in visualization, to distinguish between cognitive and affective elements, as well as between visualization and data-specific trust antecedents. We use our framework to design and run a large crowd-sourced study to quantify the role of visual complexity in establishing trust in science visualizations. Our study provides empirical evidence for several aspects of our proposed theoretical framework, most notably the impact of cognition, affective responses, and individual differences when establishing trust in visualizations. 

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2. Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content

   Lundgard, Alan; Satyanarayan, Arvind (April 2022, IEEE transactions on visualization and computer graphics)

   null (Ed.)

   Natural language descriptions sometimes accompany visualizations to better communicate and contextualize their insights, and to improve their accessibility for readers with disabilities. However, it is difficult to evaluate the usefulness of these descriptions, and how effectively they improve access to meaningful information, because we have little understanding of the semantic content they convey, and how different readers receive this content. In response, we introduce a conceptual model for the semantic content conveyed by natural language descriptions of visualizations. Developed through a grounded theory analysis of 2,147 sentences, our model spans four levels of semantic content: enumerating visualization construction properties (e.g., marks and encodings); reporting statistical concepts and relations (e.g., extrema and correlations); identifying perceptual and cognitive phenomena (e.g., complex trends and patterns); and elucidating domain-specific insights (e.g., social and political context). To demonstrate how our model can be applied to evaluate the effectiveness of visualization descriptions, we conduct a mixed-methods evaluation with 30 blind and 90 sighted readers, and find that these reader groups differ significantly on which semantic content they rank as most useful. Together, our model and findings suggest that access to meaningful information is strongly reader-specific, and that research in automatic visualization captioning should orient toward descriptions that more richly communicate overall trends and statistics, sensitive to reader preferences. Our work further opens a space of research on natural language as a data interface coequal with visualization. 

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3. Using Close Reading as a Method for Evaluating Visualizations

   https://doi.org/10.1109/BELIV51497.2020.00011  

   Bares, Annie; Zeller, Stephanie; Jackson, Cullen D.; Keefe, Daniel F.; Samsel, Francesca (October 2020, 2020 IEEE Workshop on Evaluation and Beyond - Methodological Approaches to Visualization (BELIV))

   null (Ed.)

   Visualization research and practice that incorporates the arts make claims to being more effective in connecting with users on a human level. However, these claims are difficult to measure quantitatively. In this paper, we present a follow-on study to use close reading, a humanities method from literary studies, to evaluate visualizations created using artistic processes [Bares 2020]. Close reading is a method in literary studies that we've previously explored as a method for evaluating visualizations. To use close reading as an evaluation method, we guide participants through a series of steps designed to prompt them to interpret the visualization's formal, informational, and contextual features. Here we elaborate on our motivations for using close reading as a method to evaluate visualizations, and enumerate the procedures we used in the study to evaluate a 2D visualization, including modifications made because of the COVID-19 pandemic. Key findings of this study include that close reading is an effective formative method to elicit information related to interpretation and critique; user subject position; and suspicion or skepticism. Information gained through close reading is valuable in the visualization design and iteration processes, both related to designing features and other formal elements more effectively, as well as in considering larger questions of context and framing. 

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4. "Yeah, this graph doesn't show that": Analysis of Online Engagement with Misleading Data Visualizations

   https://doi.org/10.1145/3613904.3642448  

   Lisnic, Maxim; Lex, Alexander; Kogan, Marina (May 2024, ACM)

   Attempting to make sense of a phenomenon or crisis, social media users often share data visualizations and interpretations that can be erroneous or misleading. Prior work has studied how data visualizations can mislead, but do misleading visualizations reach a broad social media audience? And if so, do users amplify or challenge misleading interpretations? To answer these questions, we conducted a mixed-methods analysis of the public's engagement with data visualization posts about COVID-19 on Twitter. Compared to posts with accurate visual insights, our results show that posts with misleading visualizations garner more replies in which the audiences point out nuanced fallacies and caveats in data interpretations. Based on the results of our thematic analysis of engagement, we identify and discuss important opportunities and limitations to effectively leveraging crowdsourced assessments to address data-driven misinformation. 

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5. Augmented Reality as a Visualization Technique for Scholarly Publications in Astronomy: An Empirical Evaluation

   https://doi.org/10.1109/VIS54172.2023.00016  

   Adams, Jane L; South, Laura; Çöltekin, Arzu; Goodman, Alyssa A; Borkin, Michelle A (October 2023, IEEE Visualization Conference)

   We present a mixed methods user study evaluating augmented reality (AR) as a visualization technique for use in astronomy journal publications. This work is motivated by the highly spatial nature of scientific visualizations employed in astronomy, including spatial reasoning tasks for hypothesis generation and scientific communications. In this 52-person user study, we evaluate two AR approaches (one traditional tabletop projection and the other with a ‘tangible’ aid) as spatial 3D visualization techniques, as compared to a baseline 3D rendering on a phone. We identify a significant difference in mental and physical workload between the two AR conditions in men and women. Qualitatively, through thematic coding of interviews, we identify notable observed differences ranging from device-specific physical challenges, to subdomain-specific utility within astronomy. The confluence of quantitative and qualitative results suggest a tension between workload and engagement when comparing non-AR and AR technologies. We summarize these findings and contribute them for reference in data visualization research furthering novel scientific communications in astronomy journal publications. 

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