DevLan/vispubs huggingface dataset:
From Asymptomatics to Zombies: Visualization-Based Education of Disease Modeling for Children
Authors: Graham Mcneill, Max Sondag, Stewart Powell, Phoebe Asplin, Cagatay Turkay, Faron Moller, Daniel Archambault
Published at CHI, 2023

Throughout the COVID-19 pandemic, visualizations became commonplace in public communications to help people make sense of the world and the reasons behind government-imposed restrictions. Though the adult population were the main target of these messages, children were affected by restrictions through not being able to see friends and virtual schooling. However, through these daily models and visualizations, the pandemic response provided a way for children to understand what data scientists really do and provided new routes for engagement with STEM subjects. In this paper, we describe the development of an interactive and accessible visualization tool to be used in workshops for children to explain computational modeling of diseases, in particular COVID-19. We detail our design decisions based on approaches evidenced to be effective and engaging such as unplugged activities and interactivity. We share reflections and learnings from delivering these workshops to 140 children and assess their effectiveness.
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse
Authors: Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
Published at CHI, 2023

Machine learning (ML) recourse techniques are increasingly used in high-stakes domains, providing end users with actions to alter ML predictions, but they assume ML developers understand what input variables can be changed. However, a recourse plan’s actionability is subjective and unlikely to match developers’ expectations completely. We present GAM Coach, a novel open-source system that adapts integer linear programming to generate customizable counterfactual explanations for Generalized Additive Models (GAMs), and leverages interactive visualizations to enable end users to iteratively generate recourse plans meeting their needs. A quantitative user study with 41 participants shows our tool is usable and useful, and users prefer personalized recourse plans over generic plans. Through a log analysis, we explore how users discover satisfactory recourse plans, and provide empirical evidence that transparency can lead to more opportunities for everyday users to discover counterintuitive patterns in ML models. GAM Coach is available at: https://poloclub.github.io/gam-coach/.
GeoCamera: Telling Stories in Geographic Visualizations with Camera Movements
Authors: Wenchao Li 0005, Zhan Wang, Yun Wang 0012, Di Weng, Liwenhan Xie, Siming Chen 0001, Haidong Zhang, Huamin Qu
Published at CHI, 2023

In geographic data videos, camera movements are frequently used and combined to present information from multiple perspectives. However, creating and editing camera movements requires significant time and professional skills. This work aims to lower the barrier of crafting diverse camera movements for geographic data videos. First, we analyze a corpus of 66 geographic data videos and derive a design space of camera movements with a dimension for geospatial targets and one for narrative purposes. Based on the design space, we propose a set of adaptive camera shots and further develop an interactive tool called GeoCamera. This interactive tool allows users to flexibly design camera movements for geographic visualizations. We verify the expressiveness of our tool through case studies and evaluate its usability with a user study. The participants find that the tool facilitates the design of camera movements.
GestureExplorer: Immersive Visualisation and Exploration of Gesture Data
Authors: Ang Li 0007, Jiazhou Liu, Maxime Cordeil, Jack Topliss, Thammathip Piumsomboon, Barrett Ens
Published at CHI, 2023

This paper presents the design and evaluation of GestureExplorer, an Immersive Analytics tool that supports the interactive exploration, classification and sensemaking with large sets of 3D temporal gesture data. GestureExplorer features 3D skeletal and trajectory visualisations of gestures combined with abstract visualisations of clustered sets of gestures. By leveraging the large immersive space afforded by a Virtual Reality interface our tool allows free navigation and control of viewing perspective for users to gain a better understanding of gestures. We explored a selection of classification methods to provide an overview of the dataset that was linked to a detailed view of the data that showed different visualisation modalities. We evaluated GestureExplorer with two user studies and collected feedback from participants with diverse visualisation and analytics backgrounds. Our results demonstrated the promising capability of GestureExplorer for providing a useful and engaging experience in exploring and analysing gesture data.
Going, Going, Gone: Exploring Intention Communication for Multi-User Locomotion in Virtual Reality
Authors: Julian Rasch, Vladislav Dmitrievic Rusakov, Martin Schmitz, Florian Müller 0003
Published at CHI, 2023

Exploring virtual worlds together with others adds a social component to the Virtual Reality (VR) experience that increases connectedness. In the physical world, joint locomotion comes naturally through implicit intention communication and subsequent adjustments of the movement patterns. In VR, however, discrete locomotion techniques such as point&teleport come without prior intention communication, hampering the collective experience. Related work proposes fixed groups, with a single person controlling the group movement, resulting in the loss of individual movement capabilities. To close the gap and mediate between these two extremes, we introduce three intention communication methods and explore them with two baseline methods. We contribute the results of a controlled experiment (n=20) investigating these methods from the perspective of a leader and a follower in a dyadic locomotion task. Our results suggest shared visualizations support the understanding of movement intentions, increasing the group feeling while maintaining individual freedom of movement.
Graphical Perception of Saliency-based Model Explanations
Authors: Yayan Zhao, Mingwei Li, Matthew Berger
Published at CHI, 2023

In recent years, considerable work has been devoted to explaining predictive, deep learning-based models, and in turn how to evaluate explanations. An important class of evaluation methods are ones that are human-centered, which typically require the communication of explanations through visualizations. And while visualization plays a critical role in perceiving and understanding model explanations, how visualization design impacts human perception of explanations remains poorly understood. In this work, we study the graphical perception of model explanations, specifically, saliency-based explanations for visual recognition models. We propose an experimental design to investigate how human perception is influenced by visualization design, wherein we study the task of alignment assessment, or whether a saliency map aligns with an object in an image. Our findings show that factors related to visualization design decisions, the type of alignment, and qualities of the saliency map all play important roles in how humans perceive saliency-based visual explanations.
Groupnamics: Designing an Interface for Overviewing and Managing Parallel Group Discussions in an Online Classroom
Authors: Arissa J. Sato, Zefan Sramek, Koji Yatani
Published at CHI, 2023

Instructors facilitating online classes have a limited ability to see and hear interactions of student groups working in parallel, which prevents them from interacting with students effectively. In this work, we explore interface design for providing an overview of parallel group discussions in online classrooms. We derive design considerations through a participatory design process and instantiate them in our visualization interface, Groupnamics. Groupnamics visualizes recent vocal activities and discussion statuses of each group in a one-page view, facilitating identification of groups where intervention may be needed. Our user study with 16 instructors confirmed that Groupnamics can successfully provide cues for when instructors should join group discussions and improvements on the perceived usefulness and ease of use over the baseline interface representing existing videoconferencing tools. Our qualitative results suggest future research directions in interface design for online parallel group discussions.
GVQA: Learning to Answer Questions about Graphs with Visualizations via Knowledge Base
Authors: Sicheng Song, Juntong Chen, Chenhui Li, Changbo Wang
Published at CHI, 2023

Graphs are common charts used to represent the topological relationship between nodes. It is a powerful tool for data analysis and information retrieval tasks involve asking questions about graphs. In formative study, we found that questions for graphs are not only about the relationship of nodes but also about the properties of graph elements. We propose a pipeline to answer natural language questions about graph visualizations and generate visual answers. We first extract the data from graphs and convert them into GML format. We design data structures to encode graph information and convert them into an knowledge base. We then extract topic entities from questions. We feed questions, entities and knowledge bases into our question-answer model to obtain the SPARQL queries for textual answers. Finally, we design a module to present the answers visually. A user study demonstrates that these visual and textual answers are useful, credible and and transparent.
Here and Now: Creating Improvisational Dance Movements with a Mixed Reality Mirror
Authors: Qiushi Zhou, Louise Grebel, Andrew Irlitti, Julie Ann Minaai, Jorge Gonçalves 0001, Eduardo Velloso
Published at CHI, 2023

This paper explores using mixed reality (MR) mirrors for supporting improvisational dance making. Motivated by the prevalence of mirrors in dance studios and inspired by Forsythe’s Improvisation Technologies, we conducted workshops with 13 dancers and choreographers to inform the design of future MR visualisation and annotation tools for dance. The workshops involved using a prototype MR mirror as a technology probe that reveals the spatial and temporal relationships between the reflected dancing body and its surroundings during improvisation; speed dating group interviews around future design ideas; follow-up surveys and extended interviews with a digital media dance artist and a dance educator. Our findings highlight how the MR mirror enriches dancers’ temporal and spatial perception, creates multi-layered presence, and affords appropriation by dancers. We also discuss the unique place of MR mirrors in the theoretical context of dance and in the history of movement visualisation, and distil lessons for broader HCI research.
How Can Deep Neural Networks Aid Visualization Perception Research? Three Studies on Correlation Judgments in Scatterplots
Authors: Fumeng Yang, Yuxin Ma, Lane Harrison, James Tompkin 0001, David H. Laidlaw
Published at CHI, 2023

How deep neural networks can aid visualization perception research is a wide-open question. This paper provides insights from three perspectives—prediction, generalization, and interpretation—via training and analyzing deep convolutional neural networks on human correlation judgments in scatterplots across three studies. The first study assesses the accuracy of twenty-nine neural network architectures in predicting human judgments, finding that a subset of the architectures (e.g., VGG-19) has comparable accuracy to the best-performing regression analyses in prior research. The second study shows that the resulting models from the first study display better generalizability than prior models on two other judgment datasets for different scatterplot designs. The third study interprets visual features learned by a convolutional neural network model, providing insights about how the model makes predictions, and identifies potential features that could be investigated in human correlation perception studies. Together, this paper suggests that deep neural networks can serve as a tool for visualization perception researchers in devising potential empirical study designs and hypothesizing about perpetual judgments. The preprint, data, code, and training logs are available at https://doi.org/10.17605/osf.io/exa8m.
How Data Analysts Use a Visualization Grammar in Practice
Authors: Xiaoying Pu, Matthew Kay 0001
Published at CHI, 2023

Visualization grammars, often based on the Grammar of Graphics (GoG), have much potential for augmenting data analysis in a programming environment. However, we do not know how analysts conceptualize grammar abstractions, or how a visualization grammar works with data analysis in practice. Therefore, we qualitatively analyzed how experienced analysts (N = 6) from TidyTuesday, a social data project, wrangled and visualized data using GoG-based ggplot2 without given tasks in R Markdown. Though participants’ analysis and customization needs could mismatch with GoG component design, their analysis processes aligned with the goal of GoG to expedite visualization iteration. We also found a feedback loop and tight coupling between visualization and data transformation code, explaining both participants’ productivity and their errors. From these results, we discuss how future visualization grammars can become more practical for analysts and how visualization grammar and analysis tools can better integrate within a programming (i.e., computational notebook) environment.
How Instructional Data Physicalisation Fosters Reflection in Personal Informatics
Authors: Marit Bentvelzen, Julia Dominiak, Jasmin Niess, Frederique Henraat, Pawel W. Wozniak
Published at CHI, 2023

The ever-increasing number of devices quantifying our lives offers a perspective of high awareness of one’s wellbeing, yet it remains a challenge for personal informatics (PI) to effectively support data-based reflection. Effective reflection is recognised as a key factor for PI technologies to foster wellbeing. Here, we investigate whether building tangible representations of health data can offer engaging and reflective experiences. We conducted a between-subjects study where n = 60 participants explored their immediate blood pressure data in relation to medical norms. They either used a standard mobile app, built a data representation from LEGO® bricks based on instructions, or completed a free-form brick build. We found that building with instructions fostered more comparison and using bricks fostered focused attention. The free-form condition required extra time to complete, and lacked usability. Our work shows that designing instructional physicalisation experiences for PI is a means of improving engagement and understanding of personal data.
iBall: Augmenting Basketball Videos with Gaze-moderated Embedded Visualizations
Authors: Zhutian Chen, Qisen Yang, Jiarui Shan, Tica Lin, Johanna Beyer, Haijun Xia, Hanspeter Pfister
Published at CHI, 2023

We present iBall, a basketball video-watching system that leverages gaze-moderated embedded visualizations to facilitate game understanding and engagement of casual fans. Video broadcasting and online video platforms make watching basketball games increasingly accessible. Yet, for new or casual fans, watching basketball videos is often confusing due to their limited basketball knowledge and the lack of accessible, on-demand information to resolve their confusion. To assist casual fans in watching basketball videos, we compared the game-watching behaviors of casual and die-hard fans in a formative study and developed iBall based on the findings. iBall embeds visualizations into basketball videos using a computer vision pipeline, and automatically adapts the visualizations based on the game context and users’ gaze, helping casual fans appreciate basketball games without being overwhelmed. We confirmed the usefulness, usability, and engagement of iBall in a study with 16 casual fans, and further collected feedback from 8 die-hard fans.
Interactive Context-Preserving Color Highlighting for Multiclass Scatterplots
Authors: Kecheng Lu, Khairi Reda, Oliver Deussen, Yunhai Wang
Published at CHI, 2023

Color is one of the main visual channels used for highlighting elements of interest in visualization. However, in multi-class scatterplots, color highlighting often comes at the expense of degraded color discriminability. In this paper, we argue for context-preserving highlighting during the interactive exploration of multi-class scatterplots to achieve desired pop-out effects, while maintaining good perceptual separability among all classes and consistent color mapping schemes under varying points of interest. We do this by first generating two contrastive color mapping schemes with large and small contrasts to the background. Both schemes maintain good perceptual separability among all classes and ensure that when colors from the two palettes are assigned to the same class, they have a high color consistency in color names. We then interactively combine these two schemes to create a dynamic color mapping for highlighting different points of interest. We demonstrate the effectiveness through crowd-sourced experiments and case studies.
Misleading Beyond Visual Tricks: How People Actually Lie with Charts
Authors: Maxim Lisnic, Cole Polychronis, Alexander Lex, Marina Kogan
Published at CHI, 2023

Data visualizations can empower an audience to make informed decisions. At the same time, deceptive representations of data can lead to inaccurate interpretations while still providing an illusion of data-driven insights. Existing research on misleading visualizations primarily focuses on examples of charts and techniques previously reported to be deceptive. These approaches do not necessarily describe how charts mislead the general population in practice. We instead present an analysis of data visualizations found in a real-world discourse of a significant global event—Twitter posts with visualizations related to the COVID-19 pandemic. Our work shows that, contrary to conventional wisdom, violations of visualization design guidelines are not the dominant way people mislead with charts. Specifically, they do not disproportionately lead to reasoning errors in posters’ arguments. Through a series of examples, we present common reasoning errors and discuss how even faithfully plotted data visualizations can be used to support misinformation.
NetworkNarratives: Data Tours for Visual Network Exploration and Analysis
Authors: Wenchao Li 0005, Sarah Schöttler, James Scott-Brown, Yun Wang 0012, Siming Chen 0001, Huamin Qu, Benjamin Bach
Published at CHI, 2023

This paper introduces semi-automatic data tours to aid the exploration of complex networks. Exploring networks requires significant effort and expertise and can be time-consuming and challenging. Distinct from guidance and recommender systems for visual analytics, we provide a set of goal-oriented tours for network overview, ego-network analysis, community exploration, and other tasks. Based on interviews with five network analysts, we developed a user interface (NetworkNarratives) and 10 example tours. The interface allows analysts to navigate an interactive slideshow featuring facts about the network using visualizations and textual annotations. On each slide, an analyst can freely explore the network and specify nodes, links, or subgraphs as seed elements for follow-up tours. Two studies, comprising eight expert and 14 novice analysts, show that data tours reduce exploration effort, support learning about network exploration, and can aid the dissemination of analysis results. NetworkNarratives is available online, together with detailed illustrations for each tour.
NFTDisk: Visual Detection of Wash Trading in NFT Markets
Authors: Xiaolin Wen, Yong Wang 0021, Xuanwu Yue, Feida Zhu 0001, Min Zhu
Published at CHI, 2023

With the growing popularity of Non-Fungible Tokens (NFT), a new type of digital assets, various fraudulent activities have appeared in NFT markets. Among them, wash trading has become one of the most common frauds in NFT markets, which attempts to mislead investors by creating fake trading volumes. Due to the sophisticated patterns of wash trading, only a subset of them can be detected by automatic algorithms, and manual inspection is usually required. We propose NFTDisk, a novel visualization for investors to identify wash trading activities in NFT markets, where two linked visualization modules are presented: a radial visualization module with a disk metaphor to overview NFT transactions and a flow-based visualization module to reveal detailed NFT flows at multiple levels. We conduct two case studies and an in-depth user interview with 14 NFT investors to evaluate NFTDisk. The results demonstrate its effectiveness in exploring wash trading activities in NFT markets.
Notable: On-the-fly Assistant for Data Storytelling in Computational Notebooks
Authors: Haotian Li 0001, Lu Ying, Haidong Zhang, Yingcai Wu, Huamin Qu, Yun Wang 0012
Published at CHI, 2023

Computational notebooks are widely used for data analysis. Their interleaved displays of code and execution results (e.g., visualizations) are welcomed since they enable iterative analysis and preserve the exploration process. However, the communication of data findings remains challenging in computational notebooks. Users have to carefully identify useful findings from useless ones, document them with texts and visual embellishments, and then organize them in different tools. Such workflow greatly increases their workload, according to our interviews with practitioners. To address the challenge, we designed Notable to offer on-the-fly assistance for data storytelling in computational notebooks. It provides intelligent support to minimize the work of documenting and organizing data findings and diminishes the cost of switching between data exploration and storytelling. To evaluate Notable, we conducted a user study with 12 data workers. The feedback from user study participants verifies its effectiveness and usability.
On the Design of AI-powered Code Assistants for Notebooks
Authors: Andrew M. McNutt, Chenglong Wang, Robert A. DeLine, Steven Mark Drucker
Published at CHI, 2023

AI-powered code assistants, such as Copilot, are quickly becoming a ubiquitous component of contemporary coding contexts. Among these environments, computational notebooks, such as Jupyter, are of particular interest as they provide rich interface affordances that interleave code and output in a manner that allows for both exploratory and presentational work. Despite their popularity, little is known about the appropriate design of code assistants in notebooks. We investigate the potential of code assistants in computational notebooks by creating a design space (reified from a survey of extant tools) and through an interview-design study (with 15 practicing data scientists). Through this work, we identify challenges and opportunities for future systems in this space, such as the value of disambiguation for tasks like data visualization, the potential of tightly scoped domain-specific tools (like linters), and the importance of polite assistants.