Through online health communities (OHCs), patients and caregivers exchange their illness experiences and strategies for overcoming the illness, and provide emotional support. To facilitate healthy and lively conversations in these communities, their members should be continuously monitored and nurtured by OHC administrators. The main challenge of OHC administrators' tasks lies in understanding the diverse dimensions of conversation threads that lead to productive discussions in their communities. In this paper, we present a design study in which three domain expert groups participated, an OHC researcher and two OHC administrators of online health communities, which was conducted to find with a visual analytic solution. Through our design study, we characterized the domain goals of OHC administrators and derived tasks to achieve these goals. As a result of this study, we propose a system called VisOHC, which visualizes individual OHC conversation threads as collapsed boxes-a visual metaphor of conversation threads. In addition, we augmented the posters' reply authorship network with marks and/or beams to show conversation dynamics within threads. We also developed unique measures tailored to the characteristics of OHCs, which can be encoded for thread visualizations at the users' requests. Our observation of the two administrators while using VisOHC showed that it supports their tasks and reveals interesting insights into online health communities. Finally, we share our methodological lessons on probing visual designs together with domain experts by allowing them to freely encode measurements into visual variables.

In this paper, we would like to investigate how people make sense of unfamiliar information visualizations. In order to achieve the research goal, we conducted a qualitative study by observing 13 participants when they endeavored to make sense of three unfamiliar visualizations (i.e., a parallel-coordinates plot, a chord diagram, and a treemap) that they encountered for the first time. We collected data including audio/video record of think-aloud sessions and semi-structured interview; and analyzed the data using the grounded theory method. The primary result of this study is a grounded model of NOvice's information Vlsualization Sensemaking (NOVIS model), which consists of the five major cognitive activities: 1 encountering visualization, 2 constructing a frame, 3 exploring visualization, 4 questioning the frame, and 5 floundering on visualization. We introduce the NOVIS model by explaining the five activities with representative quotes from our participants. We also explore the dynamics in the model. Lastly, we compare with other existing models and share further research directions that arose from our observations.

Online consumer reviews have become a substantial component of e-commerce and provide online shoppers with abundant information about products. However, previous studies provided mixed results about whether consumers experience information overload from such a vast volume of reviews. Thus, this study investigates how users perceive products depending on various numbers of reviews (from 0 to 3,000 reviews) and different review valences (generally positive, generally negative, and divided). Two crowdsourced studies with 1,783 participants were conducted. The study found no clear evidence to suggest that information overload increases as the number of reviews increases. Instead, the participants relied on a very limited number of reviews in making purchase decisions. In addition, it was observed that the review valence affected how the participants used different information sources from the interface. Based on the results, this article provides a set of interesting implications and design guidelines.

Healthcare information systems (e.g., Bar Code Medication Administration [BCMA] system) have been adopted to deliver efficient healthcare services recently. However, though it is seemingly simple to use (scanning barcodes before medication), users of the BCMA system (e.g., nurses and pharmacists) often show noncompliance behaviors. Therefore, the goal of this study is to comprehensively understand why such noncompliance behaviors occur with BCMA system. Through comprehensive literature review, 128 instances of causes were identified, which were categorized into five categories: Poor Visual and Audio Interface, Poor Physical Ergonomic Design, Poor Information Integrity, Abnormal Situation for System Use, and User Reluctance and Negligence. The results show that successful use of a BCMA system requires supportive systems and environments, so it is more like an issue of the system rather than that of an individual user or a device. It is believed that the proposed categories could be applicable in investigating noncompliance behaviors in other healthcare information systems as well.

This special section is composed of two articles, each appearing here by invitation. The authors of the articles delivered two of the top five presentations made in the Computer Systems/Internet Te...

This study investigated the impacts of three sorting techniques on various cognitive tasks performed on a tabular representation. The tasks under study were a multiattribute object selection task and selected low-level analytic tasks. Three sorting techniques, including sorting by a column (Typical Sort), sorting by all columns simultaneously (SimulSort), and sorting by all columns with faithful vertical locations (ParallelTable), were compared with a static table without the sorting feature (Baseline). An incentivized controlled laboratory study with 80 participants and a preliminary eye-tracker study were conducted to better understand the strengths and weaknesses of the four different approaches. SimulSort and ParallelTable were found to significantly improve the performance of multiattribute object selection. ParallelTable, however, suffers from an occlusion problem, so it is not an appropriate support for some low-level analytic tasks. The findings were used to propose appropriate sorting techniques for specific tasks performed on a table.

Social network analysis (SNA) is becoming increasingly concerned not only with actors and their relations, but also with distinguishing between different types of such entities. For example, social scientists may want to investigate asymmetric relations in organizations with strict chains of command, or incorporate non-actors such as conferences and projects when analyzing coauthorship patterns. Multimodal social networks are those where actors and relations belong to different types, or modes, and multimodal social network analysis (mSNA) is accordingly SNA for such networks. In this paper, we present a design study that we conducted with several social scientist collaborators on how to support mSNA using visual analytics tools. Based on an openended, formative design process, we devised a visual representation called parallel node-link bands (PNLBs) that splits modes into separate bands and renders connections between adjacent ones, similar to the list view in Jigsaw. We then used the tool in a qualitative evaluation involving five social scientists whose feedback informed a second design phase that incorporated additional network metrics. Finally, we conducted a second qualitative evaluation with our social scientist collaborators that provided further insights on the utility of the PNLBs representation and the potential of visual analytics for mSNA.

Abstract Effective visualization of dynamic graphs remains an open research topic, and many state‐of‐the‐art tools use animated node‐link diagrams for this purpose. Despite its intuitiveness, the effectiveness of animation in node‐link diagrams has been questioned, and several empirical studies have shown that animation is not necessarily superior to static visualizations. However, the exact mechanics of perceiving animated node‐link diagrams are still unclear. In this paper, we study the impact of different dynamic graph metrics on user perception of the animation. After deriving candidate visual graph metrics, we perform an exploratory user study where participants are asked to reconstruct the event sequence in animated node‐link diagrams. Based on these findings, we conduct a second user study where we investigate the most important visual metrics in depth. Our findings show that node speed and target separation are prominent visual metrics to predict the performance of event sequencing tasks.

For information visualization researchers, eye tracking has been a useful tool to investigate research participants' underlying cognitive processes by tracking their eye movements while they interact with visual techniques. We used an eye tracker to better understand why participants with a variant of a tabular visualization called `SimulSort' outperformed ones with a conventional table and typical one-column sorting feature (i.e., Typical Sorting). The collected eye-tracking data certainly shed light on the detailed cognitive processes of the participants; SimulSort helped with decision-making tasks by promoting efficient browsing behavior and compensatory decision-making strategies. However, more interestingly, we also found unexpected eye-tracking patterns with SimulSort. We investigated the cause of the unexpected patterns through a crowdsourcing-based study (i.e., Experiment 2), which elicited an important limitation of the eye tracking method: incapability of capturing peripheral vision. This particular result would be a caveat for other visualization researchers who plan to use an eye tracker in their studies. In addition, the method to use a testing stimulus (i.e., influential column) in Experiment 2 to verify the existence of such limitations would be useful for researchers who would like to verify their eye tracking results.

Time is a universal and essential aspect of data in any investigative analysis. It helps analysts establish causality, build storylines from evidence, and reject infeasible hypotheses. For this reason, many investigative analysis tools provide visual representations designed for making sense of temporal data. However, the field of visual analytics still needs more evidence explaining how temporal visualization actually aids the analysis process, as well as design recommendations for how to build these visualizations. To fill this gap, we conducted an insight-based qualitative study to investigate the influence of temporal visualization on investigative analysis. We found that visualizing temporal information helped participants externalize chains of events. Another contribution of our work is the lightweight evaluation approach used to collect, visualize, and analyze insight.

Direct manipulation has had major influence on interface design since it was proposed by Shneiderman in 1982. Although directness generally benefits users, direct manipulation also has weaknesses. In some cases, such as when a user needs to manipulate small, attribute-rich objects or multiple objects simultaneously, indirect manipulation may be more efficient at the cost of directness or intuitiveness of the interaction. Several techniques have been developed over the years to address these issues, but these are all isolated and limited efforts with no coherent underlying principle. We propose the notion of Surrogate Interaction that ties together a large subset of these techniques through the use of a surrogate object that allow users to interact with the surrogate instead of the domain object. We believe that formalizing this family of interaction techniques will provide an additional and powerful interface design alternative for interaction designers, as well as uncover opportunities for future research.

With the advent of Web 2.0, social network services (SNSs), such as Facebook and MySpace, have grown explosively and globally as one of core Web 2.0 applications. However, as revealed in other cultural comparison studies in the field of human–computer interaction, it is believed that cultural differences profoundly impact on how people use SNSs. Unfortunately, the differences in using SNSs have not been systematically investigated, so this study presents a web-based survey study among three nations: Republic of Korea, People's Republic of China, and the United States of America. It was assumed that SNS users form bridging and bonding social capital (borrowed from social capital theory) through the five functions of SNS that were categorized: Identity, Expert Search, Connection, Communication, and Contents Sharing. A correlation between social capital-related activities and usage patterns of SNS was expected. A total of 489 responded to the web-based survey through the three counties. Although the theory of cultural differences turned out to be insufficient to explain diverse usage patterns of SNSs, the results showed that Korean and Chinese users form bridging and bonding social capital mainly through Expert Search and Connection functions, but American users mainly use the Communication function to form bonding Social Capital. Various implications of these results for researchers and practitioners who work on and for SNSs are described as well.

Visualization plays a crucial role in understanding dynamic social networks at many different levels (i.e., group, subgroup, and individual). Node-link-based visualization techniques are currently widely used for these tasks and have been demonstrated to be effective, but it was found that they also have limitations in representing temporal changes, particularly at the individual and subgroup levels. To overcome these limitations, this article presents a new network visualization technique, called “TimeMatrix,” based on a matrix representation. Interaction techniques, such as overlay controls, a temporal range slider, semantic zooming, and integrated network statistical measures, support analysts in studying temporal social networks. To validate the design, the article presents a user study involving three social scientists analyzing inter-organizational collaboration data. The study demonstrates how TimeMatrix may help analysts gain insights about the temporal aspects of network data that can be subsequently tested with network analytic methods.

Beyond the Desktop Metaphor: Design Integrated Digital Work Environments has a straightforward title—it is about what will come after the desktop metaphor for our digital workplaces. The editors an...

Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a user's intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction.

Universal design (UD) is an approach to design that incorporates things which can be used by all people to the greatest extent possible. UD in information and communication technologies (ICTs) is of growing importance because standard ICTs have great potential to be usable by all people, including people with disabilities (PWDs). Currently, PWDs who need ICTs often have less access because the products have not been universally designed. We hypothesize that one of the reasons for the slow adoption of UD is that universal design resources (UDRs) are not adequate for facilitating designers' tasks. We investigated the usability of UDRs from designers' perspectives. A heuristic evaluation on eight selected UDRs was conducted, and the opinions of contributors to the content of these resources were collected through a web-based survey study. The results of the heuristic evaluation show that most of the investigated UDRs do not provide a clear central idea and fail to support the cognitive processes of designers. The results of the survey also confirmed that the content of these resources do not systematically address the needs of designers as end-users during the development process.

Interest in context-aware computing has expanded the use of sensing technologies. The accelerometer is one of the most widely used sensors for capturing context because it is small, inexpensive, lightweight, and self-operable. In efforts to obtain behavioral patterns, many studies have reported the use of multiple accelerometers attached to the human body. However, this is difficult to implement in real-life situations and may not fully address the context of user interaction. In contrast, the present study employed a single tri-axial accelerometer attached to a handheld computing device instead of to a user. The objective was to determine what contextual information could be obtained from this more feasible, albeit limited, source of acceleration data. Data analyses confirmed that changes in both mobility and lighting conditions induced statistically significant differences in the output of the accelerometer. Categories and Subject Descriptors H.5.2 [User Interfaces]: Interaction styles (e.g., commands, menus, forms, direct manipulation)

This paper examines factors that affect performance on a basic menu selection task by users who are visually healthy and users with Diabetic Retinopathy (DR) in order to inform better interface design. Linear and logistic regression models were used to examine various contextual factors that influenced task efficiency (time) and accuracy (errors). Interface characteristics such as multimodal feedback, Windows® accessibility settings, and menu item location were investigated along with various visual function and participant characteristics. Results indicated that Windows® accessibility settings and other factors, including age, computer experience, visual acuity, contrast sensitivity, and menu item location, were significant predictors of task performance.

This study investigates the effectiveness of two design interventions, the Microsoft® Windows® accessibility settings and multimodal feedback, aimed at the enhancement of a menu selection task, for users with diabetic retinopathy (DR) with stratified levels of visual dysfunction. Several menu selection task performance measures, both time- and accuracy-based, were explored across different interface conditions and across groups of participants stratified by different degrees of vision loss. The results showed that the Windows® accessibility settings had a significant positive impact on performance for participants with DR. Moreover, multimodal feedback had a negligible effect for all participants. Strategies for applying multimodal feedback to menu selection are discussed, as well as the potential benefits and drawbacks of the Windows® accessibility settings.

This paper examines factors that affect performance of a basic menu selection task by users who are visually healthy and users with Diabetic Retinopathy (DR) in order to inform better interface design. Interface characteristics such as multimodal feedback, Windows® accessibility settings, and menu item location were investigated. Analyses of Variance (ANOVA) were employed to examine the effects of interface features on task performance. Linear regression was used to further examine and model various contextual factors that influenced task performance. Results indicated that Windows® accessibility settings significantly improved performance of participants with more progressed DR. Additionally, other factors, including age, computer experience, visual acuity, and menu location were significant predictors of the time required for subjects to complete the task.