Temporal Narrative Monitoring in Dynamic Information Environments

TL;DR

This paper introduces a system-oriented framework for modeling and monitoring evolving narratives in dynamic information environments, especially during crises, using semantic embeddings and temporal clustering to improve situational awareness.

Contribution

The work presents a novel, label-free approach that captures the temporal evolution of narratives through semantic structures, enhancing understanding of crisis-related information flows.

Findings

High cluster coherence achieved

Revealed heterogeneous narrative lifecycles

Demonstrated effective temporal lifecycle analysis

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.