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| From Chaos to Spheres: Identification and Characterization of Social Risks in Short Videos of Emergencies |
| Huang Shijing1,2, Li Jiaxuan2, Shen Hongzhou3, Yuan Qinjian2 |
1.High-Quality Development Evaluation Research Institute, Nanjing University of Posts and Telecommunications, Nanjing 210003
2.School of Information Management, Nanjing University, Nanjing 210023
3.School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003 |
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Abstract During emergencies, social risks in short videos are dispersed in form, structurally unclear, and rapidly evolving. Identifying their internal structure and evolutionary paths helps improve the precise governance of public opinion risks. In this study, we developed a “from chaos to clusters” analytical framework to identify and profile in short videos during emergencies. First, from the perspective of complex systems, we demonstrated the transformation mechanisms of short-video social risks from chaos to clusters and constructed a dual-axis space defined by the information-emotion and individual-collective dimensions, providing a theoretical basis for risk-cluster identification. Second, we established a multilevel feature engineering indicator system and combined a variational autoencoder (VAE), HDBSCAN (hierarchical density-based spatial clustering of applications with noise), and K-means to identify and profile risk clusters. Finally, based on Markov transition analysis, we examined the evolutionary paths and transition mechanisms of the risk clusters. The results showed that social risks in short videos during emergencies could be classified into six major risk clusters, which differ markedly in terms of risk intensity, content characteristics, and spatial distribution. Their evolution exhibited both escalation and divergence, with evolutionary paths varying across event types. By transforming dispersed risk information into structured social risk intelligence, this study enhances the understanding of the internal structure and evolutionary patterns of short-video social risks during emergencies and provides a decision-making basis for graded early warnings and differentiated governance of public opinion risks.
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Received: 26 November 2025
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