Political sectarianism is fueled in part by misperceptions of political opponents: People commonly overestimate the support for extreme policies among members of the other party. These misperceptions inflame partisan animosity and may be used to justify extremism among one's own party. Research suggests that correcting partisan misperceptions-by informing people about the actual views of outparty members-may reduce one's own expressed support for political extremism, including partisan violence and antidemocratic actions. However, there remains a limited understanding of how the design of correction interventions drives these effects. The present study investigated how correction effects depend on different representations of outparty views communicated through data visualizations. Building on prior interventions that present the average outparty view, we consider the impact of visualizations that more fully convey the range of views among outparty members. We conducted an experiment with U.S.-based participants from Prolific (N=239 Democrats, N=244 Republicans). Participants made predictions about support for political violence and undemocratic practices among members of their political outparty. They were then presented with data from an earlier survey on the actual views of outparty members. Some participants viewed only the average response (Mean-Only condition), while other groups were shown visual representations of the range of views from 75% of the outparty (Mean+Interval condition) or the full distribution of responses (Mean+Points condition). Compared to a control group that was not informed about outparty views, we observed the strongest correction effects (i.e., lower support for political violence and undemocratic practices) among participants in the Mean-only and Mean+Points condition, while correction effects were weaker in the Mean+Interval condition. In addition, participants who observed the full distribution of out-party views (Mean+Points condition) were most accurate at later recalling the degree of support among the outparty. Our findings suggest that data visualizations can be an important tool for correcting pervasive distortions in beliefs about other groups. However, the way in which variability in outparty views is visualized can significantly shape how people interpret and respond to corrective information. Supplemental materials for this paper are available at this OSF repository.

This January 2025 issue of the IEEE Transactions on Visualization and Computer Graphics (TVCG) contains the proceedings of IEEE VIS 2024, held on October 1318 October, 2024 in St. Pete Beach, Florida, USA, with the three General Chairs Paul Rosen (University of Utah), Kristi Potter (U.S. National Renewable Energy Laboratory), and Remco Chang (Tufts University). With IEEE VIS 2024, the conference series is in its 35th year.

News articles containing data visualizations play an important role in informing the public on issues ranging from public health to politics. Recent research on the persuasive appeal of data visualizations suggests that prior attitudes can be notoriously difficult to change. Inspired by an NYT article, we designed two experiments to evaluate the impact of elicitation and contrasting narratives on attitude change, recall, and engagement. We hypothesized that eliciting prior beliefs leads to more elaborative thinking that ultimately results in higher attitude change, better recall, and engagement. Our findings revealed that visual elicitation leads to higher engagement in terms of feelings of surprise. While there is an overall attitude change across all experiment conditions, we did not observe a significant effect of belief elicitation on attitude change. With regard to recall error, while participants in the draw trend elicitation exhibited significantly lower recall error than participants in the categorize trend condition, we found no significant difference in recall error when comparing elicitation conditions to no elicitation. In a follow-up study, we added contrasting narratives with the purpose of making the main visualization (communicating data on the focal issue) appear strikingly different. Compared to the results of Study 1, we found that contrasting narratives improved engagement in terms of surprise and interest but interestingly resulted in higher recall error and no significant change in attitude. We discuss the effects of elicitation and contrasting narratives in the context of topic involvement and the strengths of temporal trends encoded in the data visualization.

Data visualizations are vital to scientific communication on critical issues such as public health, climate change, and socioeconomic policy. They are often designed not just to inform, but to persuade people to make consequential decisions (e.g., to get vaccinated). Are such visualizations persuasive, especially when audiences have beliefs and attitudes that the data contradict? In this paper we examine the impact of existing attitudes (e.g., positive or negative attitudes toward COVID-19 vaccination) on changes in beliefs about statistical correlations when viewing scatterplot visualizations with different representations of statistical uncertainty. We find that strong prior attitudes are associated with smaller belief changes when presented with data that contradicts existing views, and that visual uncertainty representations may amplify this effect. Finally, even when participants’ beliefs about correlations shifted their attitudes remained unchanged, highlighting the need for further research on whether data visualizations can drive longer-term changes in views and behavior.

Images are an indispensable part of the news we consume. Highly emotional images from mainstream and misinformation sources can greatly influence our trust in the news. We present two studies on the effects of emotional facial images on users' perception of bias in news content and the credibility of sources. In study 1, we investigate the impact of repeated exposure to content with images containing positive or negative facial expressions on users’ judgements of source credibility and bias. In study 2, we focus on sources' systematic emotional portrayal of specific politicians. Our results show the presence of negative (angry) facial emotions can lead to perceptions of higher bias in content. We also find that systematic portrayal negative portrayal of different politicians leads to lower perceptions of source credibility. These results highlight how implicit visual propositions manifested by emotions in facial expressions might have a substantial effect on our trust in news.

For many households, investing for retirement is one of the most significant decisions and is fraught with uncertainty. In a classic study in behavioral economics, Benartzi and Thaler (1999) found evidence using bar charts that investors exhibit myopic loss aversion in retirement decisions: Investors overly focus on the potential for short-term losses, leading them to invest less in riskier assets and miss out on higher long-term returns. Recently, advances in uncertainty visualizations have shown improvements in decision-making under uncertainty in a variety of tasks. In this paper, we conduct a controlled and incentivized crowdsourced experiment replicating Benartzi and Thaler (1999) and extending it to measure the effect of different uncertainty representations on myopic loss aversion. Consistent with the original study, we find evidence of myopic loss aversion with bar charts and find that participants make better investment decisions with longer evaluation periods. We also find that common uncertainty representations such as interval plots and bar charts achieve the highest mean expected returns while other uncertainty visualizations lead to poorer long-term performance and strong effects on the equity premium. Qualitative feedback further suggests that different uncertainty representations lead to visual reasoning heuristics that can either mitigate or encourage a focus on potential short-term losses. We discuss implications of our results on using uncertainty visualizations for retirement decisions in practice and possible extensions for future work.

Understanding correlation judgement is important to designing effective visualizations of bivariate data. Prior work on correlation perception has not considered how factors including prior beliefs and uncertainty representation impact such judgements. The present work focuses on the impact of uncertainty communication when judging bivariate visualizations. Specifically, we model how users update their beliefs about variable relationships after seeing a scatterplot with and without uncertainty representation. To model and evaluate the belief updating, we present three studies. Study 1 focuses on a proposed "Line + Cone" visual elicitation method for capturing users' beliefs in an accurate and intuitive fashion. The findings reveal that our proposed method of belief solicitation reduces complexity and accurately captures the users' uncertainty about a range of bivariate relationships. Study 2 leverages the "Line + Cone" elicitation method to measure belief updating on the relationship between different sets of variables when seeing correlation visualization with and without uncertainty representation. We compare changes in users beliefs to the predictions of Bayesian cognitive models which provide normative benchmarks for how users should update their prior beliefs about a relationship in light of observed data. The findings from Study 2 revealed that one of the visualization conditions with uncertainty communication led to users being slightly more confident about their judgement compared to visualization without uncertainty information. Study 3 builds on findings from Study 2 and explores differences in belief update when the bivariate visualization is congruent or incongruent with users' prior belief. Our results highlight the effects of incorporating uncertainty representation, and the potential of measuring belief updating on correlation judgement with Bayesian cognitive models.

Hierarchical clustering is an important technique to organize big data for exploratory data analysis. However, existing one-size-fits-all hierarchical clustering methods often fail to meet the diverse needs of different users. To address this challenge, we present an interactive steering method to visually supervise constrained hierarchical clustering by utilizing both public knowledge (e.g., Wikipedia) and private knowledge from users. The novelty of our approach includes 1) automatically constructing constraints for hierarchical clustering using knowledge (knowledge-driven) and intrinsic data distribution (data-driven), and 2) enabling the interactive steering of clustering through a visual interface (user-driven). Our method first maps each data item to the most relevant items in a knowledge base. An initial constraint tree is then extracted using the ant colony optimization algorithm. The algorithm balances the tree width and depth and covers the data items with high confidence. Given the constraint tree, the data items are hierarchically clustered using evolutionary Bayesian rose tree. To clearly convey the hierarchical clustering results, an uncertainty-aware tree visualization has been developed to enable users to quickly locate the most uncertain sub-hierarchies and interactively improve them. The quantitative evaluation and case study demonstrate that the proposed approach facilitates the building of customized clustering trees in an efficient and effective manner.

Recently, human behavior sensing based on WiFi channel state information has drawn more attention in the ubiquitous computing field because it can provide accurate information about the target under a device‐free scheme. This paper concentrates on user authentication applications using channel state information. We investigate state‐of‐the‐art studies and survey their characteristics. First, we introduce the concept of channel state information and outline the fundamental principle of user authentication. These systems measure the dynamic channel state information profile and implement user authentication by exploring the channel state information variation caused by users because each user generates unique channel state information fluctuations. Second, we elaborate on signal processing approaches, including signal selection and preprocessing, feature extraction, and classification methods. Third, we thoroughly investigate the latest user authentication applications. Specifically, we analyze these applications from typical human action, including gait, activity, gesture, and stillness. Finally, we provide a comprehensive discussion of user authentication and conclude the paper by presenting some open issues, research directions, and possible solutions.

We propose a visualization technique, Du Bois wrapped bar chart, inspired by work of W.E.B Du Bois. Du Bois wrapped bar charts enable better large-to-small bar comparison by wrapping large bars over a certain threshold. We first present two crowdsourcing experiments comparing wrapped and standard bar charts to evaluate (1) the benefit of wrapped bars in helping participants identify and compare values; (2) the characteristics of data most suitable for wrapped bars. In the first study (n=98) using real-world datasets, we find that wrapped bar charts lead to higher accuracy in identifying and estimating ratios between bars. In a follow-up study (n=190) with 13 simulated datasets, we find participants were consistently more accurate with wrapped bar charts when certain category values are disproportionate as measured by entropy and H-spread. Finally, in an in-lab study, we investigate participants' experience and strategies, leading to guidelines for when and how to use wrapped bar charts.

We introduce "Parallel Embeddings", a new technique that generalizes the classical Parallel Coordinates visualization technique to sequences of learned representations. This visualization technique is designed for concept-oriented "model comparison" tasks, allowing data scientists to understand qualitative differences in how models interpret input data. We compare user performance with our tool against Tensor Board Embedding Projector for understanding model accuracy and qualitative model differences. With our tool, users were more accurate and learned strategies for the tasks more quickly. Furthermore, users' analytical process in the comparison condition was positively influenced by using our tool beforehand.

Our contribution is the design and evaluation of an interactive machine learning interface that rapidly provides the user with model feedback after every interaction. To address visual scalability, this interface communicates with the user via a "tip of the iceberg" approach, where the user interacts with a small set of recommended instances for each class. To address computational scalability, we developed an O(n) classification algorithm that incorporates user feedback incrementally, and without consulting the data's underlying representation matrix. Our computational evaluation showed that this algorithm has similar accuracy to several off-the-shelf classification algorithms with small amounts of labeled data. Empirical evaluation revealed that users performed better using our design compared to an equivalent active learning setup.

We present Verifi2, a visual analytic system to support the investigation of misinformation on social media. Various models and studies have emerged from multiple disciplines to detect or understand the effects of misinformation. However, there is still a lack of intuitive and accessible tools that help social media users distinguish misinformation from verified news. Verifi2 uses state-of-the-art computational methods to highlight linguistic, network, and image features that can distinguish suspicious news accounts. By exploring news on a source and document level in Verifi2, users can interact with the complex dimensions that characterize misinformation and contrast how real and suspicious news outlets differ on these dimensions. To evaluate Verifi2, we conduct interviews with experts in digital media, communications, education, and psychology who study misinformation. Our interviews highlight the complexity of the problem of combating misinformation and show promising potential for Verifi2 as an educational tool on misinformation.

Abstract Cognitive biases are systematic errors in judgment due to an over‐reliance on rule‐of‐thumb heuristics. Recent research suggests that cognitive biases, like numerical anchoring, transfers to visual analytics in the form of visual anchoring. However, it is unclear how visualization users can be visually anchored and how the anchors affect decision‐making. To investigate, we performed a between‐subjects laboratory experiment with 94 participants to analyze the effects of visual anchors and strategy cues using a visual analytics system. The decision‐making task was to identify misinformation from Twitter news accounts. Participants were randomly assigned to conditions that modified the scenario video (visual anchor) and/or strategy cues provided. Our findings suggest that such interventions affect user activity, speed, confidence, and, under certain circumstances, accuracy. We discuss implications of our results on the forking paths problem and raise concerns on how visualization researchers train users to avoid unintentionally anchoring users and affecting the end result.

Visual text analytics has recently emerged as one of the most prominent topics in both academic research and the commercial world. To provide an overview of the relevant techniques and analysis tasks, as well as the relationships between them, we comprehensively analyzed 263 visualization papers and 4,346 mining papers published between 1992-2017 in two fields: visualization and text mining. From the analysis, we derived around 300 concepts (visualization techniques, mining techniques, and analysis tasks) and built a taxonomy for each type of concept. The co-occurrence relationships between the concepts were also extracted. Our research can be used as a stepping-stone for other researchers to 1) understand a common set of concepts used in this research topic; 2) facilitate the exploration of the relationships between visualization techniques, mining techniques, and analysis tasks; 3) understand the current practice in developing visual text analytics tools; 4) seek potential research opportunities by narrowing the gulf between visualization and mining techniques based on the analysis tasks; and 5) analyze other interdisciplinary research areas in a similar way. We have also contributed a web-based visualization tool for analyzing and understanding research trends and opportunities in visual text analytics.

Big data analytics traditionally involves download of massive amounts of datasets to common server/cluster for processing. Analytic process gets slower with increasing size of required data and network conditions. Data scientists also need explicit access to data locations to download required data. Explicit access to required data may not always be granted due to security reasons. To simplify and accelerate the analytics process on distributed big data with security considerations, we proposed the Virtual Information Fabric Infrastructure (VIFI) for data driven discoveries. Instead of moving large amounts of data to a common place of processing, VIFI allows automatic transfer of required analytics programs to the distributed data locations for in-place processing of relevant data. VIFI allows data scientists to conduct and coordinate complex analytics processes on distributed data repositories using containerization technology and open-source workflow design tools. VIFI alleviates users from having detailed knowledge of distributed data locations, as well as required dependencies, installation and configuration of analytical libraries. In this paper, we demonstrate our current and future work to improve the VIFI architecture using previous and additional uses cases, data management layer that simplifies search of relevant data sets through addition of metadata, integration with security policies at different institutions with the proposed VIFI security layer, and the use of a user-friendly web interface to carry different VIFI activities.

Learning and gaining knowledge of Roman history is an area of interest for students and citizens at large. This is an example of a subject with great sweep (with many interrelated sub-topics over, in this case, a 3,000 year history) that is hard to grasp by any individual and, in its full detail, is not available as a coherent story. In this paper, we propose a visual analytics approach to construct a data driven view of Roman history based on a large collection of Wikipedia articles. Extracting and enabling the discovery of useful knowledge on events, places, times, and their connections from large amounts of textual data has always been a challenging task. To this aim, we introduce VAiRoma, a visual analytics system that couples state-of-the-art text analysis methods with an intuitive visual interface to help users make sense of events, places, times, and more importantly, the relationships between them. VAiRoma goes beyond textual content exploration, as it permits users to compare, make connections, and externalize the findings all within the visual interface. As a result, VAiRoma allows users to learn and create new knowledge regarding Roman history in an informed way. We evaluated VAiRoma with 16 participants through a user study, with the task being to learn about roman piazzas through finding relevant articles and new relationships. Our study results showed that the VAiRoma system enables the participants to find more relevant articles and connections compared to Web searches and literature search conducted in a roman library. Subjective feedback on VAiRoma was also very positive. In addition, we ran two case studies that demonstrate how VAiRoma can be used for deeper analysis, permitting the rapid discovery and analysis of a small number of key documents even when the original collection contains hundreds of thousands of documents.

Analyzing text documents has been a key research topic in many areas. Countless approaches have been proposed to tackle this problem, and they are largely categorized into fully automated approaches (via statistical techniques) or human-involved exploratory ones (via interactive visualization). The primary purpose of this workshop is to bring together researchers from both sides and provide them with opportunities to discuss ways to harmonize the power of these two complementary approaches. The combination will allow us to push the boundary of text analytics. The detailed workshop schedule, proceedings, and agenda will be available at http://www.textvis.org.

A hash tag is defined to be a word or phrase prefixed with the symbol #. It is widely used in current social media sites including Twitter and Google+, and serves as a significant meta tag to categorize users' messages, to propagate ideas and topic trends. The use of hash tags has become an integral part of the social media culture. However, the free-form nature and the varied contexts of hash tags bring challenges: how to understand hash tags and discover their relationships? In this paper, we propose Tag-Latent Dirichlet Allocation (TLDA), a new topic modeling approach to bridge hash tags and topics. TLDA extends Latent Dirichlet Allocation by incorporating the observed hash tags in the generative process. In TLDA, a hash tag is mapped into the form of a mixture of shared topics. This representation further enables the analysis of the relationships between the hash tags. Applying our model to tweet data, we first illustrate the ability of our approach to explain hard-to-understand hash tags with topics. We also demonstrate that our approach enables users to further analyze the relationships between the hash tags.

Analyzing large textual collections has become increasingly challenging given the size of the data available and the rate that more data is being generated. Topic-based text summarization methods coupled with interactive visualizations have presented promising approaches to address the challenge of analyzing large text corpora. As the text corpora and vocabulary grow larger, more topics need to be generated in order to capture the meaningful latent themes and nuances in the corpora. However, it is difficult for most of current topic-based visualizations to represent large number of topics without being cluttered or illegible. To facilitate the representation and navigation of a large number of topics, we propose a visual analytics system--HierarchicalTopic (HT). HT integrates a computational algorithm, Topic Rose Tree, with an interactive visual interface. The Topic Rose Tree constructs a topic hierarchy based on a list of topics. The interactive visual interface is designed to present the topic content as well as temporal evolution of topics in a hierarchical fashion. User interactions are provided for users to make changes to the topic hierarchy based on their mental model of the topic space. To qualitatively evaluate HT, we present a case study that showcases how HierarchicalTopics aid expert users in making sense of a large number of topics and discovering interesting patterns of topic groups. We have also conducted a user study to quantitatively evaluate the effect of hierarchical topic structure. The study results reveal that the HT leads to faster identification of large number of relevant topics. We have also solicited user feedback during the experiments and incorporated some suggestions into the current version of HierarchicalTopics.

Displays supporting stereoscopy and head-coupled motion parallax can enhance human perception of complex 3D datasets. This has been studied extensively for datasets containing 3D surfaces and 3D networks but less for so volumetric data. Volumetric data is characterized by a heavy presence of transparency, occlusion and highly ambiguous spatial structure. There are many different rendering and visualization algorithms and interactive techniques that enhance perception of volume data and these techniques' effectiveness have been evaluated. However, the effect of VR displays on perception of volume data is less well studied. Therefore, we conduct two experiments on how various display conditions affect a participant's depth perception accuracy of a volumetric dataset. A demographic pre-questionnaire also allows us to separate the accuracy differences between participants with more and less experience with 3D games and VR. Our results show an overall benefit for stereo with head-tracking for enhancing perception of depth in volumetric data. Our study also suggests that familiarity with 3D games and VR type technology affects the users'ability to perceive such data and affects the accuracy boost due to VR displays.

Displays supporting stereopsis and head location based motion parallax can enhance human perception of complex three dimensional datasets. This has been demonstrated for datasets containing 3D surfaces and 3D networks. Yet many domains, such as medical imaging, weather and environment simulations and fluid flow, generate complex volumetric data. This poster present results of an initial formal experiment that examines the effectiveness of various display conditions on depth perception of volumetric data. There is an overall benefit for stereoscopy with head-tracking in enhancing depth perception. Further, familiarity with 3D games and VR-like hardware improves the users'ability to perceive such data.

Abstract We present a general visual analytics architecture that is designed and implemented to effectively analyze unstructured social media data on a large scale. Pipelined on a high‐performance cluster configuration, MPI processing, and interactive visual analytics interfaces, our architecture, I‐SI, closely integrates data‐driven analytical methods and user‐centered visual analytics. It creates a coherent analysis environment for identifying event structures, geographical distributions, and key indicators of emerging events. This environment supports monitoring, analyzing, and responding to latent information extracted from social media. We have applied the I‐SI architecture to collect social media data, analyze the data on a large scale and uncover the latent social phenomena. To demonstrate the efficacy and applicability of I‐SI, we describe several social media use cases in multiple domains that were evaluated by experts. The use cases demonstrate that I‐SI can benefit a range of users by constructing meaningful event structures and identifying precursors to critical events within a rich, evolving set of topics.

We present research focused on designing visual analytics (VA) systems for workers in organizational environments. We focus on business analysts and asset managers, who work collaboratively to analyze information and make decisions. Through extensive investigations in two organizational environments, we found that these users struggle with managing and analyzing information from multiple perspectives. Their current tools lack support for aggregating, organizing, and sharing such information. To address their needs, we characterized their analytic workflows, extracted specific key knowledge actions for each task commonly found in these workflows, and designed and evaluated two visual analytics systems that support and encapsulate these knowledge actions. We provide design guidelines that should be used when designing visual analytics systems, and illustrate their effectiveness with two systems built by following them.

Abstract Bridges deteriorate over their life cycles and require continuous maintenance to ensure their structural integrity, and in turn, the safety of the public. Maintaining bridges is a multi‐faceted operation that requires both domain knowledge and analytics techniques over large data sources. Although most existing bridge management systems (BMS) are very efficient at data storage, they are not as effective at providing analytical capabilities or as flexible at supporting different inspection technologies. In this paper, we present a visual analytics system that extends the capability of current BMSs. Based on a nation‐wide survey and our interviews with bridge managers, we designed our system to be customizable so that it can provide interactive exploration, information correlation, and domain‐oriented data analysis. When tested by bridge managers of the U.S. Department of Transportation, we validated that our system provides bridge managers with the necessary features for performing in‐depth analysis of bridges from a variety of perspectives that are in accordance to their typical workflow.

Traditional geospatial information visualizations often present views that restrict the user to a single perspective. When zoomed out, local trends and anomalies become suppressed and lost; when zoomed in for local inspection, spatial awareness and comparison between regions become limited. In our model, coordinated visualizations are integrated within individual probe interfaces, which depict the local data in user-defined regions-of-interest. Our probe concept can be incorporated into a variety of geospatial visualizations to empower users with the ability to observe, coordinate, and compare data across multiple local regions. It is especially useful when dealing with complex simulations or analyses where behavior in various localities differs from other localities and from the system as a whole. We illustrate the effectiveness of our technique over traditional interfaces by incorporating it within three existing geospatial visualization systems: an agent-based social simulation, a census data exploration tool, and an 3D GIS environment for analyzing urban change over time. In each case, the probe-based interaction enhances spatial awareness, improves inspection and comparison capabilities, expands the range of scopes, and facilitates collaboration among multiple users.