Narges Mahyar

Kostiantyn Kucher, Nicole Sultanum, Angel Daza et al.

Appropriate evaluation and experimental design are fundamental for empirical sciences, particularly in data-driven fields. Due to the successes in computational modeling of languages, for instance, research outcomes are having an increasingly immediate impact on end users. As the gap in adoption by end users decreases, the need increases to ensure that tools and models developed by the research communities and practitioners are reliable, trustworthy, and supportive of the users in their goals. In this position paper, we focus on the issues of evaluating visual text analytics approaches. We take an interdisciplinary perspective from the visualization and natural language processing communities, as we argue that the design and validation of visual text analytics include concerns beyond computational or visual/interactive methods on their own. We identify four key groups of challenges for evaluating visual text analytics approaches (data ambiguity, experimental design, user trust, and "big picture" concerns) and provide suggestions for research opportunities from an interdisciplinary perspective.

Pantea Haghighatkhah, Mennatallah El-Assady, Jean-Daniel Fekete et al.

Current visual text analysis approaches rely on sophisticated processing pipelines. Each step of such a pipeline potentially amplifies any uncertainties from the previous step. To ensure the comprehensibility and interoperability of the results, it is of paramount importance to clearly communicate the uncertainty not only of the output but also within the pipeline. In this paper, we characterize the sources of uncertainty along the visual text analysis pipeline. Within its three phases of labeling, modeling, and analysis, we identify six sources, discuss the type of uncertainty they create, and how they propagate.

Sheshera Mysore, Mahmood Jasim, Haoru Song et al.

Keeping up with the research literature plays an important role in the workflow of scientists - allowing them to understand a field, formulate the problems they focus on, and develop the solutions that they contribute, which in turn shape the nature of the discipline. In this paper, we examine the literature review practices of data scientists. Data science represents a field seeing an exponential rise in papers, and increasingly drawing on and being applied in numerous diverse disciplines. Recent efforts have seen the development of several tools intended to help data scientists cope with a deluge of research and coordinated efforts to develop AI tools intended to uncover the research frontier. Despite these trends indicative of the information overload faced by data scientists, no prior work has examined the specific practices and challenges faced by these scientists in an interdisciplinary field with evolving scholarly norms. In this paper, we close this gap through a set of semi-structured interviews and think-aloud protocols of industry and academic data scientists (N = 20). Our results while corroborating other knowledge workers' practices uncover several novel findings: individuals (1) are challenged in seeking and sensemaking of papers beyond their disciplinary bubbles, (2) struggle to understand papers in the face of missing details and mathematical content, (3) grapple with the deluge by leveraging the knowledge context in code, blogs, and talks, and (4) lean on their peers online and in-person. Furthermore, we outline future directions likely to help data scientists cope with the burgeoning research literature.

Mashrur Rashik, Shilpa Sweth, Nishtha Agrawal et al.

Journaling plays a crucial role in managing chronic conditions by allowing patients to document symptoms and medication intake, providing essential data for long-term care. While valuable, traditional journaling methods often rely on static, self-directed entries, lacking interactive feedback and real-time guidance. This gap can result in incomplete or imprecise information, limiting its usefulness for effective treatment. To address this gap, we introduce PATRIKA, an AI-enabled prototype designed specifically for people with Parkinson's disease (PwPD). The system incorporates cooperative conversation principles, clinical interview simulations, and personalization to create a more effective and user-friendly journaling experience. Through two user studies with PwPD and iterative refinement of PATRIKA, we demonstrate conversational journaling's significant potential in patient engagement and collecting clinically valuable information. Our results showed that generating probing questions PATRIKA turned journaling into a bi-directional interaction. Additionally, we offer insights for designing journaling systems for healthcare and future directions for promoting sustained journaling.

Mahmood Jasim, Christopher Collins, Ali Sarvghad et al.

In this study, we investigate how supporting serendipitous discovery and analysis of online product reviews can encourage readers to explore reviews more comprehensively prior to making purchase decisions. We propose two interventions -- Exploration Metrics that can help readers understand and track their exploration patterns through visual indicators and a Bias Mitigation Model that intends to maximize knowledge discovery by suggesting sentiment and semantically diverse reviews. We designed, developed, and evaluated a text analytics system called Serendyze, where we integrated these interventions. We asked 100 crowd workers to use Serendyze to make purchase decisions based on product reviews. Our evaluation suggests that exploration metrics enabled readers to efficiently cover more reviews in a balanced way, and suggestions from the bias mitigation model influenced readers to make confident data-driven decisions. We discuss the role of user agency and trust in text-level analysis systems and their applicability in domains beyond review exploration.

Alyxander Burns, Thai On, Christiana Lee et al.

Accompanying a data visualization with metadata may benefit readers by facilitating content understanding, strengthening trust, and providing accountability. However, providing this kind of information may also have negative, unintended consequences, such as biasing readers' interpretations, a loss of trust as a result of too much transparency, and the possibility of opening visualization creators with minoritized identities up to undeserved critique. To help future visualization researchers and practitioners decide what kinds of metadata to include, we discuss some of the potential benefits and risks of disclosing five kinds of metadata: metadata about the source of the underlying data; the cleaning and processing conducted; the marks, channels, and other design elements used; the people who directly created the visualization; and the people for whom the visualization was created. We conclude by proposing a few open research questions related to how to communicate metadata about visualizations.

Anna Fariha, Lucy Cousins, Narges Mahyar et al.

Traditional data systems require specialized technical skills where users need to understand the data organization and write precise queries to access data. Therefore, novice users who lack technical expertise face hurdles in perusing and analyzing data. Existing tools assist in formulating queries through keyword search, query recommendation, and query auto-completion, but still require some technical expertise. An alternative method for accessing data is Query by Example (QBE), where users express their data exploration intent simply by providing examples of their intended data. We study a state-of-the-art QBE system called SQuID, and contrast it with traditional SQL querying. Our comparative user studies demonstrate that users with varying expertise are significantly more effective and efficient with SQuID than SQL. We find that SQuID eliminates the barriers in studying the database schema, formalizing task semantics, and writing syntactically correct SQL queries, and thus, substantially alleviates the need for technical expertise in data exploration.

Mahmood Jasim, Pooya Khaloo, Somin Wadhwa et al.

Local governments still depend on traditional town halls for community consultation, despite problems such as a lack of inclusive participation for attendees and difficulty for civic organizers to capture attendees' feedback in reports. Building on a formative study with 66 town hall attendees and 20 organizers, we designed and developed CommunityClick, a communitysourcing system that captures attendees' feedback in an inclusive manner and enables organizers to author more comprehensive reports. During the meeting, in addition to recording meeting audio to capture vocal attendees' feedback, we modify iClickers to give voice to reticent attendees by allowing them to provide real-time feedback beyond a binary signal. This information then automatically feeds into a meeting transcript augmented with attendees' feedback and organizers' tags. The augmented transcript along with a feedback-weighted summary of the transcript generated from text analysis methods is incorporated into an interactive authoring tool for organizers to write reports. From a field experiment at a town hall meeting, we demonstrate how CommunityClick can improve inclusivity by providing multiple avenues for attendees to share opinions. Additionally, interviews with eight expert organizers demonstrate CommunityClick's utility in creating more comprehensive and accurate reports to inform critical civic decision-making. We discuss the possibility of integrating CommunityClick with town hall meetings in the future as well as expanding to other domains.

Alyxander Burns, Cindy Xiong, Steven Franconeri et al.

Understanding a visualization is a multi-level process. A reader must extract and extrapolate from numeric facts, understand how those facts apply to both the context of the data and other potential contexts, and draw or evaluate conclusions from the data. A well-designed visualization should support each of these levels of understanding. We diagnose levels of understanding of visualized data by adapting Bloom's taxonomy, a common framework from the education literature. We describe each level of the framework and provide examples for how it can be applied to evaluate the efficacy of data visualizations along six levels of knowledge acquisition - knowledge, comprehension, application, analysis, synthesis, and evaluation. We present three case studies showing that this framework expands on existing methods to comprehensively measure how a visualization design facilitates a viewer's understanding of visualizations. Although Bloom's original taxonomy suggests a strong hierarchical structure for some domains, we found few examples of dependent relationships between performance at different levels for our three case studies. If this level-independence holds across new tested visualizations, the taxonomy could serve to inspire more targeted evaluations of levels of understanding that are relevant to a communication goal.