• 2024 Spring Intro to Data Visualization
  • 2023 Spring Modeling User Interaction
  • 2022 Spring Modeling User Interaction
  • 2021 Fall Data Visualization
  • 2021 Spring Modeling User Interaction
  • 2020 Fall Data Visualization

I am an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. My research interests lie in the intersection between HCI, Visualization, and Data Science. I am interested in developing tools and algorithms to help people with varying abilities interact with data and visualizations.

CHI 2024
How Do Low-Vision Individuals Experience Information Visualization?
Yanan Wang, Yuhang Zhao, Yea-Seul Kim
In recent years, there has been a growing interest in enhancing the accessibility of visualizations for people with visual impairments. While much of the research has focused on improving accessibility for screen reader users, the specific needs of people with remaining vision (i.e., low-vision individuals) have been largely unaddressed. To bridge this gap, we conducted a qualitative study that provides insights into how low-vision individuals experience visualizations. Read More We found that participants utilized various strategies to examine visualizations using the screen magnifiers and also observed that the default zoom level participants use for general purposes may not be optimal for reading visualizations. We identified that partic- ipants relied on their prior knowledge and memory to minimize the traversing cost when examining visualization. Based on the findings, we motivate a personalized tool to accommodate varying visual conditions of low-vision individuals and derive the design goals and features of the tool. Read Less
CHI 2024
Erie: A Declarative Grammar for Data Sonification
Hyeok Kim, Yea-Seul Kim, Jessica Hullman
Data sonification—mapping data variables to auditory variables, such as pitch or volume—is used for data accessibility, scientific exploration, and data-driven art (e.g., museum exhibitions) among others. While a substantial amount of research has been made on effective and intuitive sonification design, software support is not commensurate, limiting researchers from fully exploring its capabilities. We contribute Erie, a declarative grammar for data sonification, that enables abstractly expressing auditory mappings. Erie supports specifying extensible tone designs (e.g., periodic wave, sampling, frequency/amplitude modulation synthesizers), various encoding channels, auditory legends, and composition options like sequencing and overlaying. Read More Using standard Web Audio and Web Speech APIs, we provide an Erie compiler for web environments. We demonstrate the expressiveness and feasibility of Erie by repli- cating research prototypes presented by prior work and provide a sonification design gallery. We discuss future steps to extend Erie to- ward other audio computing environments and support interactive data sonification. Read Less
DIS 2023
Best Paper Honorable Mention
Making Data-Driven Articles More Accessible: An Active Preference Learning Approach to Data Fact Personalization
Yanan Wang, Yea-Seul Kim
Data-driven news articles are widely used to communicate societal phenomena with concrete evidence. These articles are often accompanied by a visualization, helping readers to contextualize content. However, blind and low vision (BLV) individuals have limited access to visualizations, hindering a deep understanding of data. We explore the possibility of dynamically generating data facts (texts describing data patterns in a chart) for BLV individuals based on their preferences to aid the reading of such articles. Read More We conduct a formative study to understand how they perceive system-generated data facts and the factors influencing their preferences. The results indicate the preferences are highly varied among individuals, and a simple preference elicitation alone induces noise. Based on the findings, we developed a method to personalize the data facts generation using an active learning approach. The evaluation studies demonstrate that our model converges effectively and provides more preferable sets of data facts than the baseline. Read Less
CHI 2023
Explain What a Treemap is: Exploratory Investigation of Strategies for Explaining Unfamiliar Chart to Blind and Low Vision Users
Gyeongri Kim, Jiho Kim, Yea-Seul Kim
Visualization designers increasingly use diverse types of visualizations, but assistive technologies and education for blind and low vision people often focus on elementary chart types. We explore textual explanation as a more generalizable solution. We define three dimensions of explanation strategies based on education theories: comparing to a familiar chart type, describing how to draw one, and using a concrete example. We develop a prototype system that automatically generates text explanations from a given chart specification. Read More We conduct an exploratory study with 24 legally blind people to observe both the effectiveness and the perceived effectiveness of the strategies. The findings include: description of visual appearance is overall more effective than instructions for drawing, effective strategies differ by each chart type and by each participant, and the user's perceived effectiveness does not always lead to better performance. We demonstrate the feasibility of an explanation generation system and compile design considerations. Read Less
CHI 2023
Exploring Chart Question Answering for Blind and Low Vision Users
Jiho Kim, Arjun Srinivasan, Nam Wook Kim, Yea-Seul Kim
Data visualizations can be complex or involve numerous data points, making them impractical to navigate using screen readers alone. Question answering (QA) systems have the potential to support visualization interpretation and exploration without overwhelming blind and low vision (BLV) users. To investigate if and how QA systems can help BLV users in working with visualizations, we conducted a Wizard of Oz study with 24 BLV people where participants freely posed queries about four visualizations. Read More We collected 979 queries and mapped them to popular analytic task taxonomies. We found that retrieving value and finding extremum were the most common tasks, participants often made complex queries and used visual references, and the data topic notably influenced the queries. We compile a list of design considerations for accessible chart QA systems and make our question corpus publicly available to guide future research and development. Read Less
CHI 2023
VisLab: Enabling Visualization Designers to Gather Empirically Informed Design Feedback
Jinhan Choi, Changhoon Oh, Yea-Seul Kim, Nam Wook Kim
When creating a visualization, designers face various conflicting design choices. They typically rely on their hunches to deal with intricate trade-offs or resort to feedback from their colleagues. On the other hand, researchers have long used empirical methods to derive useful quantitative insights into visualization designs. Taking inspiration from this research tradition, we developed VisLab, an open-source online system to complement the existing qualitative feedback practice and help visualization practitioners run experiments to gather empirically informed design feedback. Read More We surveyed practitioners’ perceptions of quantitative feedback and analyzed the research literature to inform VisLab’s motivation and design. VisLab operationalizes the experiment process using templates and dashboards to make empirical methods amenable for practitioners while supporting sharing and remixing experiments to aid knowledge exchange and validation. We demonstrated the validity of experiments in VisLab and evaluated the usability and potential usefulness of VisLab in visualization design practice. Read Less
Thirty-Two Years of IEEE VIS: Authors, Fields of Study and Citations
Hongtao Hao, Yumian Cui, Zhengxiang Wang, Yea-Seul Kim
The IEEE VIS Conference (VIS) recently rebranded itself as a unified conference and officially positioned itself within the discipline of Data Science. Driven by this movement, we investigated (1) who contributed to VIS, and (2) where VIS stands in the scientific world. We examined the authors and fields of study of 3,240 VIS publications in the past 32 years based on data collected from OpenAlex and IEEE Xplore, among other sources. We also examined the citation flows from referenced papers (i.e., those referenced in VIS) to VIS, and from VIS to citing papers (i.e., those citing VIS). We found that VIS has been becoming increasingly popular and collaborative. Read More The number of publications, of unique authors, and of participating countries have been steadily growing. Both cross-country collaborations, and collaborations between educational and non-educational affiliations, namely “cross-type collaborations”, are increasing. The dominance of the US is decreasing, and authors from China are now an important part of VIS. In terms of author affiliation types, VIS is increasingly dominated by authors from universities. We found that the topics, inspirations, and influences of VIS research is limited such that (1) VIS, and their referenced and citing papers largely fall into the Computer Science domain, and (2) citations flow mostly between the same set of subfields within Computer Science. Our citation analyses showed that award-winning VIS papers had higher citations. Interactive visualizations, replication data, source code and supplementary material are available at and Read Less
Seeing What You Believe or Believing What You See? Belief Biases Correlation Estimation
Cindy Xiong, Chase Stokes, Yea-Seul Kim, Steven Franconeri
When an analyst or scientist has a belief about how the world works, their thinking can be biased in favor of that belief. Therefore, one bedrock principle of science is to minimize that bias by testing the predictions of one’s belief against objective data. But interpreting visualized data is a complex perceptual and cognitive process. Through two crowdsourced experiments, we demonstrate that supposedly objective assessments of the strength of a correlational relationship can be influenced by how strongly a viewer believes in the existence of that relationship. Read More Participants viewed scatterplots depicting a relationship between meaningful variable pairs (e.g., number of environmental regulations and air quality) and estimated their correlations. They also estimated the correlation of the same scatterplots labeled instead with generic ’X’ and ’Y’ axes. In a separate section, they also reported how strongly they believed there to be a correlation between the meaningful variable pairs. Participants estimated correlations more accurately when they viewed scatterplots labeled with generic axes compared to scatterplots labeled with meaningful variable pairs. Furthermore, when viewers believed that two variables should have a strong relationship, they overestimated correlations between those variables by an r-value of about 0.1. When they believed that the variables should be unrelated, they underestimated the correlations by an r-value of about 0.1. While data visualizations are typically thought to present objective truths to the viewer, these results suggest that existing personal beliefs can bias even objective statistical values people extract from data. Read Less
Seeing Through Sounds: Mapping Auditory Dimensions to Data and Charts for People with Visual Impairments
Ruobin Wang, Crescentia Jung, Yea-Seul Kim
Sonification can be an effective medium for people with visual impairments to understand data in visualizations. However, there are no universal design principles that apply to various charts that encode different data types. Towards generalizable principles, we conducted an exploratory experiment to assess how different auditory channels (e.g., pitch, volume) impact the data and visualization perception among people with visual impairments. Read More In our experiment, participants evaluated the intuitiveness and accuracy of the mapping of auditory channels on different data and chart types. We found that participants rated pitch to be the most intuitive, while the number of tappings and the length of sounds yielded the most accurate perception in decoding data. We study how audio channels can intuitively represent different charts and demonstrate data-level perception might not directly transfer to chart-level perception as participants reflect on visual aspects of the charts while listening to audios. We conclude by how future experiments can be designed to establish a robust ranking for creating audio charts. Read Less
VIBE: A Design Space for VIsual Belief Elicitation in Data Journalism
Shambhavi Mahajan, Alireza Karduni, Bonnie Chen, Yea-Seul Kim, Emily Wall
The process of forming, expressing, and updating beliefs from data plays a critical role in data-driven decision making. Effectively eliciting those beliefs has potential for high impact across a broad set of applications, including increased engagement with data and visualizations, personalizing visualizations, and understanding users' visual reasoning processes, which can inform improved data analysis and decision making strategies (e.g., via bias mitigation). Read More Recently, belief-driven visualizations have been used to elicit and visualize readers' beliefs in a visualization alongside data in narrative media and data journalism platforms such as the New York Times and FiveThirtyEight. However, there is little research on different aspects that constitute designing an effective belief-driven visualization. In this paper, we synthesize a design space for belief-driven visualizations based on formative and summative interviews with designers and visualization experts. The design space includes 7 main design considerations, beginning with an assumed data set, then structured according to: from who, why, when, what, and how the belief is elicited, and the possible feedback about the belief that may be provided to the visualization viewer. The design space covers considerations such as the type of data parameter with optional uncertainty being elicited, interaction techniques, and visual feedback, among others. Finally, we describe how more than 24 existing belief-driven visualizations from popular news media outlets span the design space and discuss trends and opportunities within this space. Read Less
CHI 2022
What makes web data tables accessible? Insights and a tool for rendering accessible tables for people with visual impairments
Yanan Wang, Ruobin Wang, Crescentia Jung, Yea-Seul Kim
The data table is a basic but versatile representation to communicate data. From government reports to bank statements, tables efectively carry essential data-driven information by visually organizing data using rows, columns, and other arrangements (e.g., merged cells). However, many tables online neglect the accessibility requirements for people who rely on screen readers, such as people who are blind or have low vision (BLV). Read More First, we consolidated guidelines to understand what makes a table inaccessible for BLV people. We conducted an interview study to understand the importance of tables and identify further design requirements for an accessible table. We built a tool that automatically detects HTML formatted tables online and transforms them into accessible tables. Our evaluative study demonstrates how our tool can help participants understand the table's structure and layout and support smooth navigation when the table is large and complex. Read Less
CHI 2022
Visualization Accessibility in the Wild: Challenges Faced by Visualization Designers
Shakila Joyner, Amalia Riegelhuth, Kathleen Garrity, Yea-Seul Kim, Nam Wook Kim
Data visualizations are now widely used across many disciplines. However, many of them are not easily accessible for visually impaired people. In this work, we use three-staged mixed methods to understand the current practice of accessible visualization design for visually impaired people. Read More We analyzed 95 visualizations from various venues to inspect how they are made inaccessible. To understand the rationale and context behind the design choices, we also conducted surveys with 144 practitioners in the U.S. and follow-up interviews with ten selected survey participants. Our findings include the difficulties of handling modern complex and interactive visualizations and the lack of accessibility support from visualization tools in addition to personal and organizational factors making it challenging to perform accessible design practices. Read Less
CHI 2022
Putting scientific results in perspective: Improving the communication of standardized effect sizes
Yea-Seul Kim, Jake Hofman, Daniel Goldstein
How do people form impressions of efect size when reading scientifc results? We present a series of studies on how people perceive treatment efectiveness when scientifc results are summarized in various ways. We frst show that a prevalent form of summarizing results presenting mean diferences between conditions can lead to signifcant overestimation of treatment efectiveness, and that including confdence intervals can exacerbate the problem. Read More We attempt to remedy potential misperceptions by displaying information about variability in individual outcomes in diferent formats: statements about variance, a quantitative measure of standardized efect size, and analogies that compare the treatment with more familiar efects (e.g., height diferences by age). We fnd that all of these formats substantially reduce potential misperceptions and that analogies can be as helpful as more precise quantitative statements of standardized efect size. These fndings can be applied by scientists in HCI and beyond to improve the communication of results to laypeople. Read Less
Best Paper Honorable Mention
Communicating Visualizations without Visuals: Investigation of Visualization Alternative Text for People with Visual Impairments
Crescentia Jung, Shubham Mehta Atharva Kulkarni, Yuhang Zhao, Yea-Seul Kim
Alternative text is critical in communicating graphics to people who are blind or have low vision. Especially for graphics that contain rich information, such as visualizations, poorly written or an absence of alternative texts can worsen the information access inequality for people with visual impairments. Read More In this work, we consolidate existing guidelines and survey current practices to inspect to what extent current practices and recommendations are aligned. Then, to gain more insight into what people want in visualization alternative texts, we interviewed 22 people with visual impairments regarding their experience with visualizations and their information needs in alternative texts. The study findings suggest that participants actively try to construct an image of visualizations in their head while listening to alternative texts and wish to carry out visualization tasks (e.g., retrieve specific values) as sighted viewers would. The study also provides ample support for the need to reference the underlying data instead of visual elements to reduce users' cognitive burden. Informed by the study, we provide a set of recommendations to compose an informative alternative text. Read Less
Accessible Visualization: Design Space, Opportunities, and Challenges
Nam Wook Kim, Shakila Joyner, Amalia Riegelhuth, Yea-Seul Kim
Visualizations are now widely used across disciplines to understand and communicate data. The benefit of visualizations lies in leveraging our natural visual perception. However, the sole dependency on vision can produce unintended discrimination against people with visual impairments. While the visualization field has seen enormous growth in recent years, supporting people with disabilities is much less explored. In this work, we examine approaches to support this marginalized user group, focusing on visual disabilities. We collected and analyzed papers published for the last 20 years on visualization accessibility. Read More We mapped a design space for accessible visualization that includes seven dimensions: user group, literacy task, chart type, interaction, information granularity, sensory modality, assistive technology. We described the current knowledge gap in light of the latest advances in visualization and presented a preliminary accessibility model by synthesizing findings from existing research. Finally, we reflected on the dimensions and discussed opportunities and challenges for future research. Read Less
Bayesian-Assisted Inference from Visualized Data
Yea-Seul Kim, Paula Kayongo, Madeleine Grunde-McLaughlin, Jessica Hullman
A Bayesian view of data interpretation suggests that a visualization user should update their existing beliefs about a parameter's value in accordance with the amount of information about the parameter value captured by the new observations. Extending recent work applying Bayesian models to understand and evaluate belief updating from visualizations, we show how the predictions of Bayesian inference can be used to guide more rational belief updating. We design a Bayesian inference-assisted uncertainty analogy that numerically relates uncertainty in observed data to the user's subjective uncertainty, and a posterior visualization that prescribes how a user should update their beliefs given their prior beliefs and the observed data. Read More In a pre-registered experiment on 4,800 people, we find that when a newly observed data sample is relatively small (N=158), both techniques reliably improve people's Bayesian updating on average compared to the current best practice of visualizing uncertainty in the observed data. For large data samples (N=5208), where people's updated beliefs tend to deviate more strongly from the prescriptions of a Bayesian model, we find evidence that the effectiveness of the two forms of Bayesian assistance may depend on people's proclivity toward trusting the source of the data. We discuss how our results provide insight into individual processes of belief updating and extit{subjective} uncertainty, and how understanding these aspects of interpretation paves the way for more sophisticated interactive visualizations for analysis and communication. Read Less
ISS 2019
Inking Your Insights: Investigating Digital Externalization Behaviors During Data Analysis
Yea-Seul Kim, Nathalie Henry Riche, Bongshin Lee, Matthew Brehmer, Michel Pahud, Ken Hinckley, Jessica Hullman
Externalizing one's thoughts can be helpful during data analysis, such as which one marks interesting data, notes hypotheses, and draws diagrams. In this paper, we present two exploratory studies conducted to investigate types and use of externalizations during the analysis process. We first studied how people take notes during different stages of data analysis using VoyagerNote, a visualization recommendation system augmented to support text annotations, and coupled with participants' favorite external note-taking tools (e.g., word processor, pen & paper). Externalizations manifested mostly as notes written on paper or in a word processor, with annotations atop views used almost exclusively in the initial phase of analysis. Read More In the second study, we investigated two specific opportunities: (1) integrating digital pen input to facilitate the use of free-form externalizations and (2) providing a more explicit linking between visualizations and externalizations. We conducted the study with VoyagerInk, a visualization system that enabled free-form externalization with a digital pen as well as touch interactions to link externalizations to data. Participants created more graphical externalizations with VoyagerInk and revisited over half of their externalizations via the linking mechanism. Reflecting on the findings from these two studies, we discuss implications for the design of data analysis tools. Read Less
CHI 2019
A Bayesian Cognition Approach to Improve Data Visualization
Yea-Seul Kim, Logan A Walls, P. M. Krafft, Jessica Hullman
Visualization evaluation guidelines rarely account for the influence of users' prior beliefs before encountering a visualization. We demonstrate a Bayesian cognitive model for understanding how people interpret visualizations in light of prior beliefs and show how this model provides a guide for improving visualization designs. Read More In a first study, we show how applying a Bayesian cognition model to a simple visualization scenario indicates that people's judgments are consistent with a hypothesis that they are doing approximate Bayesian inference. In a second study, we evaluate how sensitive our observations of Bayesian behavior are to different techniques for eliciting people subjective distributions, and to different datasets. We find that people don't behave consistently with Bayesian predictions for large sample size datasets, and this difference cannot be explained by elicitation technique. In a final study, we show how normative Bayesian inference can be used as an evaluation metric for visualizations of uncertainty. Read Less
CHI 2019
Vocal Shortcuts for Creative Experts
Yea-Seul Kim, Mira Dontcheva, Eytan Adar, Jessica Hullman
Vocal shortcuts, short spoken phrases to control interfaces, have the potential to reduce cognitive and physical costs of interactions. They may benefit expert users of creative applications (e.g., designers, illustrators) by helping them maintain creative focus. To aid the design of vocal shortcuts and gather use cases and design guidelines for speech interaction, we interviewed ten creative experts. Read More Based on our findings, we built VoiceCuts, a prototype implementation of vocal shortcuts in the context of an existing creative application. In contrast to other speech interfaces, VoiceCuts targets experts' unique needs by handling short and partial commands and leverages document model and application context to disambiguate user utterances. We report on the viability and limitations of our approach based on feedback from creative experts. Read Less
CHI 2018
Improving Comprehension of Measurements Using Concrete Re-expression Strategies
Jessica Hullman, Yea-Seul Kim, Francis Nguyen, Lauren Speers, Maneesh Agrawala
It can be difficult to understand physical measurements (e.g., 28 lb, 600 gallons) that appear in news stories, data reports, and other documents. We develop tools that automatically re-express unfamiliar measurements using the measurements of familiar objects. Our work makes three contributions: (1) we identify effectiveness criteria for objects used in concrete measurement re-expressions; (2) we operationalize these criteria in a scalable method for mining a large dataset of concrete familiar objects with their physical dimensions from Amazon and Wikipedia; and (3) we develop automated concrete reexpression tools that implement three common re-expression strategies (adding familiar context, reunitization and proportional analogy) as energy minimization algorithms. Read More Crowdsourced evaluations of our tools indicate that people find news articles with re-expressions more helpful and re-expressions help them to better estimate new measurements. Read Less
Data Through Others' Eyes: The Impact of Visualizing Others' Expectations on Visualization Interpretation
Yea-Seul Kim, Katharina Reinecke, Jessica Hullman
In addition to visualizing input data, interactive visualizations have the potential to be social artifacts that reveal other people's perspectives on the data. However, how such social information embedded in a visualization impacts a viewer's interpretation of the data remains unknown. Inspired by recent interactive visualizations that display people's expectations of data against the data, we conducted a controlled experiment to evaluate the effect of showing social information in the form of other people's expectations on people's ability to recall the data, the degree to which they adjust their expectations to align with the data, and their trust in the accuracy of the data. Read More We found that social information that exhibits a high degree of consensus lead participants to recall the data more accurately relative to participants who were exposed to the data alone. Additionally, participants trusted the accuracy of the data less and were more likely to maintain their initial expectations when other people's expectations aligned with their own initial expectations but not with the data. We conclude by characterizing the design space for visualizing others' expectations alongside data. Read Less
Imagining Replications: Graphical Prediction & Discrete Visualizations Improve Recall & Estimation of Effect Uncertainty
Jessica Hullman, Matthew Kay, Yea-Seul Kim, Samana Shrestha
People often have erroneous intuitions about the results of uncertain processes, such as scientific experiments. Many uncertainty visualizations assume considerable statistical knowledge, but have been shown to prompt erroneous conclusions even when users possess this knowledge. Active learning approaches as well as discrete (frequency) formats for probability information have been shown to improve statistical reasoning, but are rarely applied in visualizing uncertainty in scientific reports. We present a controlled study to evaluate the impact of an alternative, interactive graphical prediction technique for communicating uncertainty in experiment results. Read More Using our technique, users sketch their prediction of the uncertainty in experimental effects prior to viewing the true sampling distribution from an experiment. We find that having a user graphically predict the possible effects from experiment replications is an effective way to improve one's ability to make predictions about replications of new experiments. Additionally, visualizing uncertainty as a set of discrete outcomes, as opposed to a continuous probability distribution, can improve recall of a sampling distribution from a single experiment. Our work has implications for various applications where it is important to elicit peoples' estimates of probability distributions and to communicate uncertainty effectively. Read Less
CHI 2017
Best Paper Award
Explaining the Gap: Visualizing One's Predictions Improves Recall and Comprehension of Data
Yea-Seul Kim, Katharina Reinecke, Jessica Hullman
Information visualizations use interactivity to enable userdriven querying of visualized data. However, users' interactions with their internal representations, including their expectations about data, are also critical for a visualization to support learning. We present multiple graphically-based techniques for eliciting and incorporating a user's prior knowledge about data into visualization interaction. Read More We use controlled experiments to evaluate how graphically eliciting forms of prior knowledge and presenting feedback on the gap between prior knowledge and the observed data impacts a user's ability to recall and understand the data. We find that participants who are prompted to reflect on their prior knowledge by predicting and self-explaining data outperform a control group in recall and comprehension. These effects persist when participants have moderate or little prior knowledge on the datasets. We discuss how the effects differ based on text versus visual presentations of data. We characterize the design space of graphical prediction and feedback techniques and describe design recommendations. Read Less
EMNLP 2016
SimpleScience: Lexical Simplification of Scientific Terminology
Yea-Seul Kim, Jessica Hullman, Matthew Burgess, Eytan Adar
Lexical simplification of scientific terms represents a unique challenge due to the lack of a standard parallel corpora and fast rate at which vocabulary shift along with research. We introduce SimpleScience, a lexical simplification approach for scientific terminology. Read More We use word embeddings to extract simplification rules from a parallel corpora containing scientific publications and Wikipedia. To evaluate our system we construct SimpleSciGold, a novel gold standard set for science-related simplifications. We find that our approach outperforms prior context-aware approaches at generating simplifications for scientific terms. Read Less
CHI 2016
Generating Personalized Spatial Analogies for Distances and Areas
Yea-Seul Kim, Jessica Hullman, Maneesh Agrawala
Distances and areas frequently appear in text articles. However, people struggle to understand these measurements when they cannot relate them to measurements of locations that they are personally familiar with. We contribute tools for generating personalized spatial analogies: re-expressions that contextualize spatial measurements in terms of locations with similar measurements that are more familiar to the user. Read More Our automated approach takes a user's location and generates a personalized spatial analogy for a target distance or area using landmarks. We present an interactive application that tags distances, areas, and locations in a text article and presents personalized spatial analogies using interactive maps. We find that users who view a personalized spatial analogy map generated by our system rate the helpfulness of the information for understanding a distance or area 1.9 points higher (on a 7 pt scale) than when they see the article with no spatial analogy and 0.7 points higher than when they see generic spatial analogy. Read Less
Users' Music Information Needs and Behaviors: Design Implications for Music Information Retrieval Systems
Jin Ha Lee, Hyerim Cho, Yea-Seul Kim
User studies in the music information retrieval (MIR) domain tend to be exploratory and qualitative in nature, involving a small number of users, which makes it difficult to derive broader implications for system design. In order to fill this gap, we conducted a large-scale user survey questioning various aspects of people's music information needs and behaviors. In particular, we investigate if general music users' needs and behaviors have significantly changed over time by comparing our current survey result with a similar survey conducted in 2004. Read More In this paper, we present the key findings from the survey data and discuss 4 emergent themes (a) the shift in access and use of personal music collections; (b) the growing need for tools to support collaborative music seeking, listening, and sharing; (c) the importance of visual music experiences; and (d) the need for ontologies for providing rich contextual information. We conclude by making specific recommendations for improving the design of MIR systems and services. Read Less
ISMIR 2016
Elucidating User Behavior in Music Services through Persona and Gender
John Fuller, Lauren Hubener, Yea-Seul Kim, Jin Ha Lee
Prior user studies in the music information retrieval field have identified different personas representing the needs, goals, and characteristics of specific user groups for a usercentered design of music services. However, these personas were derived from a qualitative study involving a small number of participants and their generalizability has not been tested. Read More The objectives of this study are to explore the applicability of seven user personas, developed in prior research, with a larger group of users and to identify the correlation between personas and the use of different types of music services. In total, 962 individuals were surveyed in order to understand their behaviors and preferences when interacting with music streaming services. Using a stratified sampling framework, key characteristics of each persona were extracted to classify users into specific persona groups. Responses were also analyzed in relation to gender, which yielded significant differences. Our findings support the development of more targeted approaches in music services rather than a universal service model. Read Less
ISMIR 2016
A Look at the Cloud from Both Sides Now: An Analysis of Cloud Music Service Usage
Jin Ha Lee, Yea-Seul Kim, Chris Hubbles
Despite the increasing popularity of cloud-based music services, few studies have examined how users select and utilize these services, how they manage and access their music collections in the cloud, and the issues or challenges they are facing within these services. Read More In this paper, we present findings from an online survey with 198 responses collected from users of commercial cloud music services, exploring their selection criteria, use patterns, perceived limitations, and future predictions. We also investigate differences in these aspects by age and gender. Our results elucidate previously under-studied changes in music consumption, music listening behaviors, and music technology adoption. The findings also provide insights into how to improve the future design of cloud-based music services, and have broader implications for any cloudbased services designed for managing and accessing personal media collections. Read Less
Computation + Journalism 2015
DeScipher: A Text Simplification Tool for Science Journalism
Yea-Seul Kim, Jessica Hullman, Eytan Adar
Complex jargon often makes scientific work less accessible to the general public. By employing a set of specific reporting strategies, journalists bridge these groups by delivering information about scientific advances in a readable, engaging way. One such strategy is using simpler terms in place of complex jargon. To assist in this process, we introduce DeScipher, a text editor application that suggests and ranks possible simplifications of complex terminology to a journalist while she is authoring an article. Read More DeScipher applies simplification rules derived from a large collection of scientific abstracts and associated author summaries, and accounts for textual context in making suggestions to the journalist. In evaluating our system, we show that DeScipher is a viable application for producing useful simplifications of scientific and other terms by comparing to prior techniques used on other corpora. We also propose concrete opportunities for future development of 'journalist-in-the-loop' tools for aiding journalists in enacting science reporting strategies. Read Less
CHI 2015
VIZMO Game Browser: Accessing Video Games by Visual Style and Mood
Jin-Ha Lee, Sungsoo Hong, Hyerim Cho, Yea-Seul Kim
Despite the growing interests in video games as consumer products as well as objects of research, current methods for accessing video games are limited. We present Vizmo as a new way of browsing video games based on their visual style and mood. Read More In order to test the usability and usefulness of Vizmo, we asked 19 video game experts to evaluate their interaction with the tool. The results show that experts perceived Vizmo as a novel and aesthetically pleasing game discovery tool which would be most useful for game research on historical and aesthetic aspects. We discuss five key points for improving the design of Vizmo as well as our future plan for the next iteration of this prototype game browser. Read Less
CHI 2014
Traffigram: Distortion for Clarification via Isochronal Cartography
Sungsoo Hong, Yea-Seul Kim, Jong-Chul Yoon, Cecilia R. Aragon
Most geographic maps visually represent physical distance; however, travel time can in some cases be more important than distance because it directly indicates availability. The technique of creating maps from temporal data is known as isochronal cartography, and is a form of distortion for clarification. Read More In an isochronal map, congestion expands areas, while ideal travel conditions make the map shrink in comparison to the actual distance scale of a traditional map. Although there have been many applications of this technique, detailed user studies of its efficacy remain scarce, and there are conflicting views on its practical value. To attempt to settle this issue, we utilized a usercentered design process to determine which features of isochronal cartography might be most usable in practice. We developed an interactive cartographic visualization system, Traffigram, that features a novel combination of efficient isochronal map algorithms and an interface designed to give map users a quick and seamless experience while preserving geospatial integrity and aesthetics. We validated our design choices with multiple usability studies. We present our results and discuss implications for design. Read Less