Temporal Narrative Monitoring in Dynamic Information Environments
This work addresses the challenge of temporal comprehension in crisis information environments for decision-makers, though it appears incremental as it builds on existing methods like clustering and embeddings.
The paper tackled the problem of modeling evolving narratives in dynamic information environments, such as crisis events, by developing a framework that uses semantic embeddings, clustering, and temporal linkage to represent narratives as adaptive entities, with results showing high cluster coherence and heterogeneous narrative lifecycles.
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.