Temporal Narrative Monitoring in Dynamic Information Environments

2026-03-18

AI Summary

This work addresses the challenge of modeling the dynamic and highly abstract evolution of information narratives during crisis events, a task where existing approaches are largely confined to static snapshots. We propose the first framework that integrates situated cognition theory with unsupervised temporal modeling, enabling adaptive representation of narrative entity trajectories within a shared semantic space. By combining semantic embeddings, density-based clustering, and rolling time-window linkage, our method requires no predefined labels and captures fine-grained narrative lifecycles, revealing heterogeneous evolution patterns characterized by coexisting transient fragments and stable anchors. Experiments on real-world crisis data demonstrate high clustering consistency and the ability to effectively identify diverse narrative evolution pathways, offering interpretable temporal representations for dynamic information monitoring and decision-making.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Natural Language Processing: Information Extraction
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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.

Problem

• Temporal Narrative Monitoring
• Dynamic Information Environments
• Crisis Events
• Information Environment
• Narrative Evolution

Innovation

• temporal narrative modeling
• semantic embedding
• density-based clustering
• rolling temporal linkage
• situational awareness