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Large Language Models (LLMs) are transforming Conversational Visual Analytics (CVA) by enabling data analysis through natural language. However, evaluating LLMs for CVA remains a challenge: requiring programming expertise, overlooking real-world complexity, and lacking interpretable metrics for multi-format (visualizations and text) outputs. Through interviews with 22 CVA developers and 16 end-users, we identified use cases, evaluation criteria and workflows. We present Lexara, a user-centered evaluation toolkit for CVA that operationalizes these insights into: (i) test cases spanning real-world scenarios; (ii) interpretable metrics covering visualization quality (data fidelity, semantic alignment, functional correctness, design clarity) and language quality (factual grounding, analytical reasoning, conversational coherence) using rule-based and LLM-as-a-Judge methods; and (iii) an interactive toolkit enabling experimental setup and multi-format and multi-level exploration of results without programming expertise. We conducted a two-week diary study with six CVA developers, drawn from our initial cohort of 22. Their feedback demonstrated Lexara's effectiveness for guiding appropriate model and prompt selection.

Data-driven storytelling has gained prominence in journalism and other data reporting fields. However, the process of creating these stories remains challenging, often requiring the integration of effective visualizations with compelling narratives to form a cohesive, interactive presentation. To help streamline this process, we present an integrated authoring framework and system, DataWeaver, that supports both visualization-to-text and text-to-visualization composition. DataWeaver enables users to create data narratives anchored to data facts derived from \"call-out\" interactions, i.e., user-initiated highlights of visualization elements that prompt relevant narrative content. In addition to this \"vis-to-text\" composition, DataWeaver also supports a \"text-initiated\" approach, generating relevant interactive visualizations from existing narratives. Key findings from an evaluation with 13 participants highlighted the utility and usability of DataWeaver and the effectiveness of its integrated authoring framework. The evaluation also revealed opportunities to enhance the framework by refining filtering mechanisms and visualization recommendations and better support authoring creativity by introducing advanced customization options.

Text in dashboards plays multiple critical roles, including providing context, offering insights, guiding interactions, and summarizing key information. Despite its importance, most dashboarding tools focus on visualizations and offer limited support for text authoring. To address this gap, we developed Plume, a system to help authors craft effective dashboard text. Through a formative review of exemplar dashboards, we created a typology of text parameters and articulated the relationship between visual placement and semantic connections, which informed Plume's design. Plume employs large language models (LLMs) to generate contextually appropriate content and provides guidelines for writing clear, readable text. A preliminary evaluation with 12 dashboard authors explored how assisted text authoring integrates into workflows, revealing strengths and limitations of LLM-generated text and the value of our human-in-the-loop approach. Our findings suggest opportunities to improve dashboard authoring tools by better supporting the diverse roles that text plays in conveying insights.

Spatial computing presents new opportunities for immersive data storytelling, yet there is limited guidance on how to build such experiences or adapt traditional narrative visualizations to this medium. We introduce a toolkit, RÉCITKIT for supporting spatial data narratives in head-mounted display (HMD) environments. The toolkit allows developers to create interactive dashboards, tag data attributes as spatial assets to 3D models and immersive scenes, generate text and audio narratives, enabling dynamic filtering, and hierarchical drill-down data discoverability. To demonstrate the utility of the toolkit, we developed Charles Minard's historical flow map of Napoleon's 1812 campaign in Russia as an immersive experience on Apple Vision Pro. We conducted a preliminary evaluation with 21 participants that comprised two groups: developers, who evaluated the toolkit by authoring spatial stories and consumers, who provided feedback on the Minard app's narrative clarity, interaction design, and engagement. Feedback highlighted how spatial interactions and guided narration enhanced insight formation, with participants emphasizing the benefits of physical manipulation (e.g., gaze, pinch, navigation) for understanding temporal and geographic data. Participants also identified opportunities for future enhancement, including improved interaction affordance visibility, customizable storytelling logic, and integration of contextual assets to support user orientation. These findings contribute to the broader discourse on toolkit-driven approaches to immersive data storytelling across domains such as education, decision support, and exploratory analytics.

There is increased interest in the interplay between text and visuals in the field of data visualization. However, this attention has predominantly been on the use of text in standalone visualizations or augmenting text stories supported by a series of independent views. In this paper, we shift from the traditional focus on single-chart annotations to characterize the nuanced but crucial communication role of text in the complex environment of interactive dashboards. Through a survey and analysis of 190 dashboards in the wild, plus 13 expert interview sessions with experienced dashboard authors, we highlight the distinctive nature of text as an integral component of the dashboard experience, while delving into the categories, semantic levels, and functional roles of text, and exploring how these text elements are coalesced by dashboard authors to guide and inform dashboard users. Our contributions are: 1) we distill qualitative and quantitative findings from our studies to characterize current practices of text use in dashboards, including a categorization of text-based components and design patterns; 2) we leverage current practices and existing literature to propose, discuss, and validate recommended practices for text in dashboards, embodied as 12 heuristics that underscore the semantic and functional role of text in offering navigational cues, contextualizing data insights, supporting reading order, etc; 3) we reflect on our findings to identify gaps and propose opportunities for data visualization researchers to push the boundaries on text usage for dashboards, from authoring support and interactivity to text generation and content personalization. Our research underscores the significance of elevating text as a first-class citizen in data visualization, and the need to support the inclusion of textual components and their interactive affordances in dashboard design.

Integrating textual content, such as titles, annotations, and captions, with visualizations facilitates comprehension and takeaways during data exploration. Yet current tools often lack mechanisms for integrating meaningful long-form prose with visual data. This paper introduces DASH, a bimodal data exploration tool that supports integrating semantic levels into the interactive process of visualization and text-based analysis. DASH operationalizes a modified version of Lundgard et al.'s semantic hierarchy model that categorizes data descriptions into four levels ranging from basic encodings to high-level insights. By leveraging this structured semantic level framework and a large language model's text generation capabilities, DASH enables the creation of data-driven narratives via drag-and-drop user interaction. Through a preliminary user evaluation, we discuss the utility of DASH's text and chart integration capabilities when participants perform data exploration with the tool.

Relevant language describing trends in data can be useful for generating summaries to help with readers' takeaways. However, the language employed in these often template-generated summaries tends to be simple, ranging from describing simple statistical information (e.g., extrema and trends) without additional context and richer language to provide actionable insights. Recent advances in Large Language Models (LLMs) have shown promising capabilities in capturing subtle nuances in language when describing information. This workshop paper specifically explores how LLMs can provide more actionable insights when describing trends by focusing on three dimensions of analytical narrative structure: semantic, rhetorical, and pragmatic. Building on prior research that examines visual and linguistic signatures for univariate line charts, we examine how LLMs can further leverage the semantic dimension of analytical narratives using quantified semantics to describe shapes in trends as people intuitively view them. These semantic descriptions help convey insights in a way that leads to a pragmatic outcome, i.e., a call to action, persuasion, warning vs. alert, and situational awareness. Finally, we identify rhetorical implications for how well these generated narratives align with the perceived shape of the data, thereby empowering users to make informed decisions and take meaningful actions based on these data insights.

Relevant language describing visual features in charts can be useful for authoring captions and summaries about the charts to help with readers' takeaways. To better understand the interplay between concepts that describe visual features and the semantic relationships among those concepts (e.g., 'sharp increase' vs. 'gradual rise'), we conducted a crowdsourced study to collect labels and visual feature pairs for univariate line charts. Using this crowdsourced dataset of labeled visual signatures, this paper proposes a novel method for labeling visual chart features based on combining feature-word distributions with the visual features and the data domain of the charts. These feature-word-topic models identify word associations with similar yet subtle differences in semantics, such as 'flat,' 'plateau,' and 'stagnant,' and descriptors of the visual features, such as 'sharp increase,' 'slow climb,' and 'peak.' Our feature-word-topic model is computed using both a quantified semantics approach and a signal processing-inspired least-errors shape-similarity approach. We finally demonstrate the application of this dataset for annotating charts and generating textual data summaries.

Conversational interfaces are increasingly used for data analysis, enabling data workers to express complex analytical intents in natural language. Yet, these interactions unfold as long, linear transcripts that are misaligned with the iterative, nonlinear nature of real-world analyses. Revisiting and summarizing conversations for different contexts is therefore challenging. This paper investigates how data workers navigate, make sense of, and communicate prior analytical conversations. To study behaviors beyond those supported by standard interfaces (i.e., scrolling and keyword search), we develop a design probe that supplements analytical conversations with structured elements and affordances (e.g., filtering, multi-level navigation and detail-on-demand). In a user study (n = 10), participants used the probe to navigate and communicate past analyses, fulfilling information needs (recall, reorient, prioritize) through navigation strategies (visual recall, sequential and abstractive) and summarization practices (adding process details and context). Based on these findings, we discuss design implications to support re-visitation and communication of analytical conversations.

Chatbots have garnered interest as conversational interfaces for a variety of tasks. While general design guidelines exist for chatbot interfaces, little work explores analytical chatbots that support conversing with data. We explore Gricean Maxims to help inform the basic design of effective conversational interaction. We also draw inspiration from natural language interfaces for data exploration to support ambiguity and intent handling. We ran Wizard of Oz studies with 30 participants to evaluate user expectations for text and voice chatbot design variants. Results identified preferences for intent interpretation and revealed variations in user expectations based on the interface affordances. We subsequently conducted an exploratory analysis of three analytical chatbot systems (text + chart, voice + chart, voice-only) that implement these preferred design variants. Empirical evidence from a second 30-participant study informs implications specific to data-driven conversation such as interpreting intent, data orientation, and establishing trust through appropriate system responses.