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Interactive Narrative Analytics: Bridging Computational Narrative Extraction and Human Sensemaking

Brian Keith

TL;DR

The paper defines Interactive Narrative Analytics (INA) as a nascent, interdisciplinary field that combines computational narrative extraction with interactive visual analytics to support sensemaking in large, text-rich information environments. It argues that existing narrative extraction methods lack scalability, transparency, interactivity, and knowledge integration, and proposes INA as a framework integrating five core components: scalable computational architectures, narrative-focused visualizations, semantic interaction, knowledge resources, and evaluation metrics. The authors outline theoretical foundations from visual analytics, narrative theory, sensemaking, and knowledge representation, and discuss current approaches, challenges, and opportunities across architectures, visualizations, interactions, knowledge integration, and evaluation. They also discuss future directions, including advanced narrative models, incremental/adaptive extraction, human-AI collaboration, and governance to address misinformation, privacy, and fairness. Overall, INA aims to transform how analysts discover and reason about evolving narratives, with practical impact across news analysis, intelligence, science, and social media domains by enabling integrated, interactive, and knowledge-enhanced narrative sensemaking.

Abstract

Information overload and misinformation create significant challenges in extracting meaningful narratives from large news collections. This paper defines the nascent field of Interactive Narrative Analytics (INA), which combines computational narrative extraction with interactive visual analytics to support sensemaking. INA approaches enable the interactive exploration of narrative structures through computational methods and visual interfaces that facilitate human interpretation. The field faces challenges in scalability, interactivity, knowledge integration, and evaluation standardization, yet offers promising opportunities across news analysis, intelligence, scientific literature exploration, and social media analysis. Through the combination of computational and human insight, INA addresses complex challenges in narrative sensemaking.

Interactive Narrative Analytics: Bridging Computational Narrative Extraction and Human Sensemaking

TL;DR

The paper defines Interactive Narrative Analytics (INA) as a nascent, interdisciplinary field that combines computational narrative extraction with interactive visual analytics to support sensemaking in large, text-rich information environments. It argues that existing narrative extraction methods lack scalability, transparency, interactivity, and knowledge integration, and proposes INA as a framework integrating five core components: scalable computational architectures, narrative-focused visualizations, semantic interaction, knowledge resources, and evaluation metrics. The authors outline theoretical foundations from visual analytics, narrative theory, sensemaking, and knowledge representation, and discuss current approaches, challenges, and opportunities across architectures, visualizations, interactions, knowledge integration, and evaluation. They also discuss future directions, including advanced narrative models, incremental/adaptive extraction, human-AI collaboration, and governance to address misinformation, privacy, and fairness. Overall, INA aims to transform how analysts discover and reason about evolving narratives, with practical impact across news analysis, intelligence, science, and social media domains by enabling integrated, interactive, and knowledge-enhanced narrative sensemaking.

Abstract

Information overload and misinformation create significant challenges in extracting meaningful narratives from large news collections. This paper defines the nascent field of Interactive Narrative Analytics (INA), which combines computational narrative extraction with interactive visual analytics to support sensemaking. INA approaches enable the interactive exploration of narrative structures through computational methods and visual interfaces that facilitate human interpretation. The field faces challenges in scalability, interactivity, knowledge integration, and evaluation standardization, yet offers promising opportunities across news analysis, intelligence, scientific literature exploration, and social media analysis. Through the combination of computational and human insight, INA addresses complex challenges in narrative sensemaking.
Paper Structure (17 sections, 5 figures, 1 table)

This paper contains 17 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Defining Interactive Narrative Analytics
  3. Distinguishing INA from Related Fields
  4. Core Principles and Objectives
  5. Integration of Existing Approaches
  6. Theoretical Foundations
  7. Core Elements: Current Approaches and Research Challenges
  8. The Need for Interactive Narrative Analytics
  9. Computational Architectures for Narrative Processing
  10. Visualization Approaches for Narrative Structures
  11. Interaction Mechanisms for Narrative Exploration
  12. Knowledge Resources for Narrative Enhancement
  13. Evaluation Approaches for Narrative Quality
  14. Ethical Considerations in Narrative Analytics
  15. Future Research Directions
  16. ...and 2 more sections

Figures (5)

  • Figure 1: The five core components of Interactive Narrative Analytics and their interconnections. Each component addresses specific challenges while working together in an integrated system.
  • Figure 2: Multi-scale narrative visualization showing different levels of granularity while maintaining context across levels.
  • Figure 3: Before-and-after comparison showing how external knowledge enhances a narrative about semiconductor export restrictions with contextual information, historical patterns, and domain expertise.
  • Figure 4: Comparison between the traditional linear pipeline approach and the Interactive Narrative Analytics integrated approach with continuous feedback between computational and human processes.
  • Figure 5: Mockup of an Interactive Narrative Analytics interface showing a narrative map visualization with semantic interaction capabilities and knowledge integration features.