DATAWEAVER: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text

2025-03-29

AI Summary

Data-driven narrative creation faces challenges in visual-textual co-design and weak semantic alignment. To address these, we propose DataWeaver, an interactive data narrative authoring framework that introduces the first bidirectional vis-to-text and text-to-vis co-generation paradigm. It anchors narrative semantics via user-initiated “call-out” interactions—highlighting key data points—to jointly govern narrative logic and chart generation. The framework integrates interactive visualization, controllable natural language generation (NLG), context-aware visualization recommendation, and cross-modal semantic alignment modeling. A 13-participant user study demonstrates its effectiveness: DataWeaver improves narrative authoring efficiency by 42% over baseline tools, and significantly enhances logical-chart consistency (p < 0.01). This work provides a scalable, methodology-driven system for accessible, high-fidelity data storytelling.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Web Mining and Content Analysis: Web data visualization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• Integrating visualizations with narratives for data-driven storytelling
• Streamlining creation of interactive data-driven narratives
• Supporting bidirectional composition between text and visualizations

Innovation

• Integrated visualization and text composition framework
• Vis-to-text and text-to-visualization bidirectional authoring
• Data-driven narratives via interactive call-out interactions