DevLan/vispubs huggingface dataset:
Seeing Through the Overlap: The Impact of Color and Opacity on Depth Order Perception in Visualization
Authors: Zhiyuan Meng, Yunpeng Yang, Qiong Zeng, Kecheng Lu, Lin Lu 0001, Changhe Tu, Fumeng Yang, Yunhai Wang
Published at CHI, 2025

Semi-transparent visualizations are commonly used to reveal information in overlapped regions by applying colors and opacity. While a few studies made recommendations on how to choose colors and opacity levels to maintain depth perception, they often conflict and overlook the interaction effect between these factors. In this paper, we systematically explore the impact of color and opacity on depth order perception across eight colors, three opacity levels, and various layer orders and arrangements. Our inferential analysis shows that both color hue and opacity significantly influence depth order perception, with the effectiveness depending on their interaction. We also derived 12 features for predictive analysis, achieving the best mean accuracy of 80.72% and mean F1 score of 87.75%, with opacity assigned to the front layer as the top feature for most models. Finally, we provide a small design tool and four guidelines to better align the design rules of colors and opacity in semi-transparent visualizations.
SeeThroughBody: Mitigating Occlusion through Body Transparency to Enhance Foot-Floor Touch Interaction
Authors: Meng Ting Shih, Chun-Jui Chou, Tzu-Wei Mi, Liwei Chan 0001
Published at CHI, 2025

Occlusion, often caused by the user's body or fingers, can significantly reduce the efficiency and usability of touch interfaces. As foot-based interactions in HMDs become more prevalent, self-occlusion becomes a more pronounced issue due to the involvement of the body and legs. This work presents SeeThroughBody, a body-rendering approach designed to mitigate occlusion and enhance touch interactions between the foot and interactive floor in virtual environments. Our user study unveiled twofold results. First, changing VisualizationStyles and BodyPartsVisibility can improve objective performance (e.g., time, movement) by reducing occlusion. Second, these modifications also affect the subjective user experience (e.g., embodiment, usability). Different VisualizationStyles and BodyPartsVisibility have varying impacts, presenting trade-offs between performance and experience. Based on these insights, we recommend Transparent-Foot and Outline-Foot for interactions focused on efficiency, and Transparent-All and Transparent-Thigh for enhancing overall user experience. Finally, we demonstrate the application of these recommendations in a map browsing scenario using foot touch.
Sequential Visual Cues from Gaze Patterns: Reasoning Assistance for Bar Charts
Authors: Antonia Schlieder, Jan Rummel, Peter Albers, Filip Sadlo
Published at CHI, 2025

Even for well-studied visual reasoning tasks such as those performed on bar charts, little is known about the cognitive strategies users adopt to solve them. Guidance systems that support users in learning visual reasoning require information on successful strategies to help unsuccessful users improve or change their strategies. We introduce the guidance paradigm of sequential visual cues (SVCs), accompanied by a differential pattern mining approach that determines relevant visual attention patterns from gaze data, and exemplified for bar charts. The novel feature of SVCs is to give hints on critical fragments of successful strategies, guiding users where to look in a visualization and in which order, but not what to do with this information. Results from an empirical study (N=30) show how critical patterns of successful and unsuccessful strategies differ for various bar chart tasks. In a qualitative survey (N=5), we explore how to surface relevant gaze patterns as SVCs.
SocialEyes: Scaling Mobile Eye-tracking to Multi-person Social Settings
Authors: Shreshth Saxena, Areez Visram, Neil Lobo, Zahid Mirza, Mehak Rafi Khan, Biranugan Pirabaharan, Alexander Nguyen, Lauren K. Fink
Published at CHI, 2025

Eye movements provide a window into human behaviour, attention, and interaction dynamics. Challenges in real-world, multi-person environments have, however, restrained eye-tracking research predominantly to single-person, in-lab settings. We developed a system to stream, record, and analyse synchronised data from multiple mobile eye-tracking devices during collective viewing experiences (e.g., concerts, films, lectures). We implemented lightweight operator interfaces for real-time-monitoring, remote-troubleshooting, and gaze-projection from individual egocentric perspectives to a common coordinate space for shared gaze analysis. We tested the system in a live concert and a film screening with 30 simultaneous viewers during each of two public events (N=60). We observe precise time-synchronisation between devices measured through recorded clock-offsets, and accurate gaze-projection in challenging dynamic scenes. Our novel analysis metrics and visualizations illustrate the potential of collective eye-tracking data for understanding collaborative behaviour and social interaction. This advancement promotes ecological validity in eye-tracking research and paves the way for innovative interactive tools.
SpatIO: Spatial Physical Computing Toolkit Based on Extended Reality
Authors: Seung Hyeon Han, Yeeun Han, Kyeongho Park, Sangjun Lee, Woohun Lee
Published at CHI, 2025

Proper placement of sensors and actuators is one of the key factors when designing spatial and proxemic interactions. However, current physical computing tools do not effectively support placing components in three-dimensional space, often forcing designers to build and test prototypes without precise spatial configuration. To address this, we propose the concept of spatial physical computing and present SpatIO, an XR-based physical computing toolkit that supports a continuous end-to-end workflow. SpatIO consists of three interconnected subsystems: SpatIO Environment for composing and testing prototypes with virtual sensors and actuators, SpatIO Module for converting virtually placed components into physical ones, and SpatIO Code for authoring interactions with spatial visualization of data flow. Through a comparative user study with 20 designers, we found that SpatIO significantly altered workflow order, encouraged broader exploration of component placement, enhanced spatial correlation between code and components, and promoted in-situ bodily testing.
SPECTRA: Personalizable Sound Recognition for Deaf and Hard of Hearing Users through Interactive Machine Learning
Authors: Steven M. Goodman, Emma J. McDonnell, Jon E. Froehlich, Leah Findlater
Published at CHI, 2025

We introduce SPECTRA, a novel pipeline for personalizable sound recognition designed to understand DHH users' needs when collecting audio data, creating a training dataset, and reasoning about the quality of a model. To evaluate the prototype, we recruited 12 DHH participants who trained personalized models for their homes. We investigated waveforms, spectrograms, interactive clustering, and data annotating to support DHH users throughout this workflow, and we explored the impact of a hands-on training session on their experience and attitudes toward sound recognition tools. Our findings reveal the potential for clustering visualizations and waveforms to enrich users' understanding of audio data and refinement of training datasets, along with data annotations to promote varied data collection. We provide insights into DHH users' experiences and perspectives on personalizing a sound recognition pipeline. Finally, we share design considerations for future interactive systems to support this population.
SpeechCompass: Enhancing Mobile Captioning with Diarization and Directional Guidance via Multi-Microphone Localization
Authors: Artem Dementyev, Dimitri Kanevsky, Samuel J. Yang, Mathieu Parvaix, Chiong Lai, Alex Olwal
Published at CHI, 2025

Speech-to-text capabilities on mobile devices have proven helpful for hearing and speech accessibility, language translation, note-taking, and meeting transcripts. However, our foundational large-scale survey (n=263) shows that the inability to distinguish and indicate speaker direction makes them challenging in group conversations. SpeechCompass addresses this limitation through real-time, multi-microphone speech localization, where the direction of speech allows visual separation and guidance (e.g., arrows) in the user interface. We introduce efficient real-time audio localization algorithms and custom sound perception hardware, running on a low-power microcontroller with four integrated microphones, which we characterize in technical evaluations. Informed by a large-scale survey (n=494), we conducted an in-person study of group conversations with eight frequent users of mobile speech-to-text, who provided feedback on five visualization styles. The value of diarization and visualizing localization was consistent across participants, with everyone agreeing on the value and potential of directional guidance for group conversations.
StructVizor: Interactive Profiling of Semi-Structured Textual Data
Authors: Yanwei Huang, Yan Miao, Di Weng, Adam Perer, Yingcai Wu
Published at CHI, 2025

Data profiling plays a critical role in understanding the structure of complex datasets and supporting numerous downstream tasks, such as social media analytics and financial fraud detection. While existing research predominantly focuses on structured data formats, a substantial portion of semi-structured textual data still requires ad-hoc and arduous manual profiling to extract and comprehend its internal structures. In this work, we propose StructVizor, an interactive profiling system that facilitates sensemaking and transformation of semi-structured textual data. Our tool mainly addresses two challenges: a) extracting and visualizing the diverse structural patterns within data, such as how information is organized or related, and b) enabling users to efficiently perform various wrangling operations on textual data. Through automatic data parsing and structure mining, StructVizor enables visual analytics of structural patterns, while incorporating novel interactions to enable profile-based data wrangling. A comparative user study involving 12 participants demonstrates the system's usability and its effectiveness in supporting exploratory data analysis and transformation tasks.
TableCanoniser: Interactive Grammar-Powered Transformation of Messy, Non-Relational Tables to Canonical Tables
Authors: Kai Xiong, Cynthia A. Huang, Michael Wybrow, Yingcai Wu
Published at CHI, 2025

TableCanoniser is a declarative grammar and interactive system for constructing relational tables from messy tabular inputs such as spreadsheets. We propose the concept of axis alignment to categorise input types and characterise the expanded scope of our system relative to existing tools. The declarative grammar consists of match conditions, which specify repeating patterns of input cells, and extract operations, which specify how matched values map to the output table. In the interactive interface, users can specify match and extract patterns by interacting with an input table, or author more advanced specifications in the coding panel. To refine and verify specifications, users interact with grammar-based provenance visualisations such as linked highlighting of input and output values, tree-based visualisation of matching patterns, and a mini-map overview of matched instances of patterns with annotations showing where cells are extracted to. We motivate and illustrate our work with real-world usage scenarios and workflows.
TangibleNet: Synchronous Network Data Storytelling through Tangible Interactions in Augmented Reality
Authors: Kentaro Takahira, Wong Kam-Kwai, Leni Yang, Xian Xu, Takanori Fujiwara, Huamin Qu
Published at CHI, 2025

Synchronous data-driven storytelling with network visualizations presents significant challenges due to the complexity of real-time manipulation of network components. While existing research addresses asynchronous scenarios, there is a lack of effective tools for live presentations. To address this gap, we developed TangibleNet, a projector-based AR prototype that allows presenters to interact with node-link diagrams using double-sided magnets during live presentations. The design process was informed by interviews with professionals experienced in synchronous data storytelling and workshops with 14 HCI/VIS researchers. Insights from the interviews helped identify key design considerations for integrating physical objects as interactive tools in presentation contexts. The workshops contributed to the development of a design space mapping user actions to interaction commands for node-link diagrams. Evaluation with 12 participants confirmed that TangibleNet supports intuitive interactions and enhances presenter autonomy, demonstrating its effectiveness for synchronous network-based data storytelling.
The Anatomy of a Plea: How Uncertainty, Visualizations & Individual Differences Shape Plea Bargain Decisions
Authors: Melanie Bancilhon, Alvitta Ottley, Andrew Jordan
Published at CHI, 2025

Plea bargains are commonly used in the criminal justice system, where they can offer potential benefits to both the prosecution and the defendant. However, research has shown that defendants often engage in poor decision-making, such as accepting the plea even when the trial sentence is likely to be less severe. While previous studies have shown some evidence that uncertainty visualizations can improve decision-making, there is a lack of research on their effectiveness in domain-specific tasks like plea bargain decision-making. In this work, we conduct a series of experiments to explore whether the presence and format of uncertainty impact plea bargain decisions, taking into account time pressure and individual differences. Our findings reveal that these factors can have a significant impact on plea bargain decisions. We also show evidence that communicating uncertainty in the form of text can elicit more optimal decisions under time-pressure conditions.
The Gulf of Interpretation: From Chart to Message and Back Again
Authors: Christian Knoll 0004, Torsten Möller, Kathleen Gregory, Laura Koesten
Published at CHI, 2025

Charts are used to communicate data visually, but often, we do not know whether a chart's intended message aligns with the message readers perceive. In this mixed-methods study, we investigate how data journalists encode data and how members of a broad audience engage with, experience, and understand these visualizations. We conducted workshops and interviews with school and university students, job seekers, designers, and senior citizens to collect perceived messages and feedback on eight real-world charts. We analyzed these messages and compared them to the intended message. Our results help to understand the gulf that can exist between messages (that producers encode) and viewer interpretations. In particular, we find that consumers are often overwhelmed with the amount of data provided and are easily confused with terms that are not well known. Chart producers tend to follow strong conventions on how to visually encode particular information that might not always benefit consumers.
The Interaction Geography Slicer: Designing Exploratory Spatial Data Visualization Tools for Teachers' Reflective Practice
Authors: Ben Rydal Shapiro, Elizabeth C. Metts, Edwin Zhao
Published at CHI, 2025

Researchers in HCI and teacher education have long recognized the potential of visualization to support teachers' reflective practice. Despite much progress however, teacher educators continue to highlight the need for more dynamic classroom data visualizations to better support teachers' reflective practice, particularly about spatial dimensions of their pedagogy. In response, this article makes three contributions. First, we build on prior work to present the Interaction Geography Slicer (IGS), an open-source tool to dynamically visualize movement, conversation, and video data over space and time in settings such as classrooms. Second, we share findings from a participatory design-based research project involving 11 experienced high school mathematics teachers who used the IGS over one year to support their reflective practice. Finally, we propose new directions for exploratory spatial classroom data visualization.
The Many Tendrils of the Octopus Map
Authors: Eduardo Puerta, Shani Claire Spivak, Michael Correll
Published at CHI, 2025

Conspiratorial thinking can connect many distinct or distant ills to a central cause. This belief has visual form in the octopus map: a map where a central force (for instance a nation, an ideology, or an ethnicity) is depicted as a literal or figurative octopus, with extending tendrils. In this paper, we explore how octopus maps function as visual arguments through an analysis of historical examples as well as a through a crowd-sourced study on how the underlying data and the use of visual metaphors contribute to specific negative or conspiratorial interpretations. We find that many features of the data or visual style can lead to "octopus-like" thinking in visualizations, even without the use of an explicit octopus motif. We conclude with a call for a deeper analysis of visual rhetoric, and an acknowledgment of the potential for the design of data visualizations to contribute to harmful or conspiratorial thinking.
The Social Construction of Visualizations: Practitioner Challenges and Experiences of Visualizing Race and Gender
Authors: Priya Dhawka, Sayamindu Dasgupta
Published at CHI, 2025

Data visualizations are increasingly seen as socially constructed, with several recent studies positing that perceptions and interpretations of visualization artifacts are shaped through complex sets of interactions between members of a community. However, most of these works have focused on audiences and researchers, and little is known about if and how practitioners account for the socially constructed framing of data visualization. In this paper, we study and analyze how visualization practitioners understand the influence of their beliefs, values, and biases in their design processes and the challenges they experience. In 17 semi-structured interviews with designers working with race and gender demographic data, we find that a complex mix of factors interact to inform how practitioners approach their design process—including their personal experiences, values, and their understandings of power, neutrality, and politics. Based on our findings, we suggest a series of implications for research and practice in this space.
Toward Filling a Critical Knowledge Gap: Charting the Interactions of Age with Task and Visualization
Authors: Zack While, Ali Sarvghad
Published at CHI, 2025

We present the results of a study comparing the performance of younger adults (YA) and people in late adulthood (PLA) across ten low-level analysis tasks and five basic visualizations, employing Bayesian regression to aggregate and model participant performance. We analyzed performance at the task level and across combinations of tasks and visualizations, reporting measures of performance at aggregate and individual levels. These analyses showed that PLA on average required more time to complete tasks while demonstrating comparable accuracy. Furthermore, at the individual level, PLA exhibited greater heterogeneity in task performance as well as differences in best-performing visualization types for some tasks. We contribute empirical knowledge on how age interacts with analysis task and visualization type and use these results to offer actionable insights and design recommendations for aging-inclusive visualization design. We invite the visualization research community to further investigate aging-aware data visualization. Supplementary materials can be found at https://osf.io/a7xtz/.
Toward Personalizable AI Node Graph Creative Writing Support: Insights on Preferences for Generative AI Features and Information Presentation Across Story Writing Processes
Authors: Hua Xuan Qin, Guangzhi Zhu, Mingming Fan 0001, Pan Hui 0001
Published at CHI, 2025

As story writing requires diverse resources, a single system combining these resources could improve personalization. We leverage the broad capabilities of generative AI to support both more general story writing needs and an understudied but essential aspect: reflection on the moral (lesson) conveyed. Through a formative study (N=12), a user study (N=14), and external evaluation (N=19), we designed, implemented, then studied a prototype plugin for FigJam supporting visualization of the story structure through customizable node graph editing, LLM audience impersonation (chatbot and non-chatbot interfaces), and image and audio generative AI features. Our findings support writers' preference for leveraging unique interplays of our breadth of features to satisfy shifting needs across writing processes, from conveying a moral across audience groups to story writing in general. We discuss how our tool design and findings can inform model bias, personalized writing support, and visualization research.
Traversing Dual Realities: Investigating Techniques for Transitioning 3D Objects between Desktop and Augmented Reality Environments
Authors: Tobias Rau, Tobias Isenberg 0001, Andreas Köhn, Michael Sedlmair, Benjamin Lee 0001
Published at CHI, 2025

Desktop environments can integrate augmented reality (AR) head-worn devices to support 3D representations, visualizations, and interactions in a novel yet familiar setting. As users navigate across the dual realities—desktop and AR—a way to move 3D objects between them is needed. We devise three baseline transition techniques based on common approaches in the literature and evaluate their usability and practicality in an initial user study (N=18). After refining both our transition techniques and the surrounding technical setup, we validate the applicability of the overall concept for real-world activities in an expert user study (N=6). In it, computational chemists followed their usual desktop workflows to build, manipulate, and analyze 3D molecular structures, but now aided with the addition of AR and our transition techniques. Based on our findings from both user studies, we provide lessons learned and takeaways for the design of 3D object transition techniques in desktop + AR environments.
Trustworthy by Design: The Viewer's Perspective on Trust in Data Visualization
Authors: Oen G. McKinley, Saugat Pandey, Alvitta Ottley
Published at CHI, 2025

Despite the importance of viewers' trust in data visualization, there is a lack of research on the viewers' own perspective on their trust. In addition, much of the research on trust remains relatively theoretical and inaccessible for designers. This work aims to address this gap by conducting a qualitative study to explore how viewers perceive different data visualizations and how their perceptions impact their trust. Three dominant themes emerged from the data. First, users appeared to be consistent, listing similar rationale for their trust across different stimuli. Second, there were diverse opinions about what factors were most important to trust perception and about why the factors matter. Third, despite this disagreement, there were important trends to the factors that users reported as impactful. Finally, we leverage these themes to give specific and actionable guidelines for visualization designers to make more trustworthy visualizations.