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Temporal Narrative Monitoring in Dynamic Information Environments

David Farr, Stephen Prochaska, Jack Moody, Lynnette Hui Xian Ng, Iain Cruickshank, Kate Starbird, Jevin West

Abstract

Comprehending the information environment (IE) during crisis events is challenging due to the rapid change and abstract nature of the domain. Many approaches focus on snapshots via classification methods or network approaches to describe the IE in crisis, ignoring the temporal nature of how information changed over time. This work presents a system-oriented framework for modeling emerging narratives as temporally evolving semantic structures without requiring prior label specification. By integrating semantic embeddings, density-based clustering, and rolling temporal linkage, the framework represents narratives as persistent yet adaptive entities within a shared semantic space. We apply the methodology to a real-world crisis event and evaluate system behavior through stratified cluster validation and temporal lifecycle analysis. Results demonstrate high cluster coherence and reveal heterogeneous narrative lifecycles characterized by both transient fragments and stable narrative anchors. We ground our approach in situational awareness theory, supporting perception and comprehension of the IE by transforming unstructured social media streams into interpretable, temporally structured representations. The resulting system provides a methodology for monitoring and decision support in dynamic information environments.

Temporal Narrative Monitoring in Dynamic Information Environments

Abstract

Comprehending the information environment (IE) during crisis events is challenging due to the rapid change and abstract nature of the domain. Many approaches focus on snapshots via classification methods or network approaches to describe the IE in crisis, ignoring the temporal nature of how information changed over time. This work presents a system-oriented framework for modeling emerging narratives as temporally evolving semantic structures without requiring prior label specification. By integrating semantic embeddings, density-based clustering, and rolling temporal linkage, the framework represents narratives as persistent yet adaptive entities within a shared semantic space. We apply the methodology to a real-world crisis event and evaluate system behavior through stratified cluster validation and temporal lifecycle analysis. Results demonstrate high cluster coherence and reveal heterogeneous narrative lifecycles characterized by both transient fragments and stable narrative anchors. We ground our approach in situational awareness theory, supporting perception and comprehension of the IE by transforming unstructured social media streams into interpretable, temporally structured representations. The resulting system provides a methodology for monitoring and decision support in dynamic information environments.
Paper Structure (23 sections, 2 figures)

This paper contains 23 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Situational Awareness
  4. Natural Language Tools Applied to Social Media Data
  5. Classification
  6. Network-Based Methods
  7. Temporal Approaches
  8. System Overview
  9. Methods
  10. Data Collection
  11. Semantic Representation and Similarity
  12. Temporal Clustering
  13. Representative Post Selection
  14. Generative Summarization and Labeling
  15. System Implementation and Results
  16. ...and 8 more sections

Figures (2)

  • Figure 1: System architecture for temporal narrative monitoring. External events drive ingestion into a shared semantic space where temporal clustering produces evolving narrative clusters in a semantic state space. Centroid-based monitoring and generative summaries support analyst review and adaptive feedback.
  • Figure :