Tracking the Temporal Dynamics of News Coverage of Catastrophic and Violent Events
Organizations: Department of Computing Sciences, Villanova University, Villanova, PA, USA
Abstract
The modern news cycle has been fundamentally reshaped by the rapid exchange of information online. As a result, media framing shifts dynamically as new information, political responses, and social reactions emerge. Understanding how these narratives form, propagate, and evolve is essential for interpreting public discourse during moments of crisis. In this study, we examine the temporal and semantic dynamics of reporting for violent and catastrophic events using a large-scale corpus of 126,602 news articles collected from online publishers. We quantify narrative change through publication volume, semantic drift, semantic dispersion, and term relevance. Our results show that sudden events of impact exhibit structured and predictable news-cycle patterns characterized by rapid surges in coverage, early semantic drift, and gradual declines toward the baseline. In addition, our results indicate the terms that are driving the temporal patterns.
Figures & tables
| Event | Onset date | Category |
|---|---|---|
| Virginia Beach shooting | 2019-05-31 | Violence |
| El Paso shooting | 2019-08-03 | Violence |
| Buffalo shooting | 2022-05-14 | Violence |
| Uvalde shooting | 2022-05-24 | Violence |
| Monterey Park shooting | 2023-01-21 | Violence |
| Boulder firebombing attack | 2025-05-30 | Violence |
Semantic Change Detection
Semantic change detection identifies how meanings, concepts, or scene contents differ across time in text, images, and other data.
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