Towards Collective Storytelling: Investigating Audience Annotations in Data Visualizations.

2025-03-04

IEEE Computer Graphics and Applications

AI Summary

This study investigates how personal annotations in data visualizations facilitate collective narrative construction, particularly within the context of COVID-19—focusing on shared memory, social interaction, and reconceptualized understanding. Methodologically, it integrates interactive log analysis, survey data, annotation text mining, in-depth interviews, and critical cartographic theory. Results demonstrate that embedded annotations function as primary social cues—exerting greater influence on interpretation than the visualization’s underlying data encoding—and actively foster empathetic reflection and situated self-narration. Annotations significantly strengthen intersubjective story linkage and experiential reflection among viewers; serve as visual anchors that guide navigation paths; and deepen socially situated comprehension. This work establishes a novel human-centered paradigm for interactive data visualization, advancing theoretical and practical frameworks for collective meaning-making in socio-technical contexts.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data visualization
• Semantics and Knowledge: Representation, semantic annotation, enhancement, enrichments, access and/or integration of a variety of data on the Web

Abstract

This work investigates personal perspectives in visualization annotations as devices for collective data-driven storytelling. Inspired by existing efforts in critical cartography, we show how people share personal memories in a visualization of COVID-19 data and how comments by other visualization readers influence the reading and understanding of visualizations. Analyzing interaction logs, reader surveys, visualization annotations, and interviews, we find that reader annotations help other viewers relate to other people's stories and reflect on their own experiences. Further, we found that annotations embedded directly into the visualization can serve as social traces guiding through a visualization and help readers contextualize their own stories. With that, they supersede the attention paid to data encodings and become the main focal point of the visualization.

Problem

• Explores personal perspectives in visualization annotations for collective storytelling
• Examines how audience comments influence COVID-19 data interpretation
• Assesses embedded annotations as social guides in visualizations

Innovation

• Using annotations for collective storytelling
• Embedding social traces in visualizations
• Annotations override data encoding focus