Translating risk narratives in socio-technical systems into infrastructure utilization metrics during compounding hazard events - H.M. Imran Kays; Khondhaker Al Momin; Kanthasamy K. Muraleetharan; Arif Mohaimin Sadri (Transportation Research Interdisciplinary Perspectives)
Risk communication in times of disasters is complex, involving rapid and diverse communication in social networks as well as limited mobilization capacity and operational constraints of physical infrastructure networks. Despite a growing literature on infrastructure interdependencies and co-dependent social-physical systems, an in-depth understanding of how risk communication in online social networks weighs into physical infrastructure networks during major disasters remains limited, let alone in compounding risk events. This study analyzes large-scale datasets of crisis mobility and activity-related social interactions and concerns available through Twitter (now ’X’) for communities impacted by an ice storm in October 2020 in Oklahoma. Compounded by the COVID-19 pandemic, the ice storm caused significant traffic disruptions due to excessive ice accumulation. By using Twitter’s academic Application Programming Interface (API) that provides complete and technically unbiased data, geotagged tweets (∼25.7 K) were collected covering the entire Oklahoma. First, the study employes natural language processing techniques, such as topic model and BERT model to classify crisis narratives (i.e., tweets), and text quantification techniques to analyze them. Next, the geotagged quantified tweets are transformed into a weighting factor for the transportation network utilization during disaster by employing spatial analysis. Finally, using network analysis, this study develops an infrastructure risk map that integrates vulnerabilities of the co-located road network. The findings reveal that this approach can uncover significant critical infrastructure disruptions during compounding disasters. By mapping such risks, the study provides emergency management agencies with situational awareness, facilitating more efficient resource allocation and prioritization aimed at enhancing disaster response efforts.