A Graph-based Approach for Detecting Critical Infrastructure Disruptions on Social Media in Disasters

Fan, Chao
Mostafavi, Ali
Yao, Wenlin
Huang, Ruihong
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The objective of this paper is to propose and test a graph-based approach for detection of critical infrastructure disruptions in social media data in disasters. Understanding the situation and disruptive events of critical infrastructure is essential to effective disaster response and recovery of communities. The potential of social media data for situation awareness during disasters has been highlighted in recent studies. However, the application of social sensing in detecting disruptions of critical infrastructure is limited because existing approaches cannot provide complete and non-ambiguous situational information about critical infrastructure. Therefore, to address this methodological gap, we developed a graph-based approach including data filtering, burst time-frame detection, content similarity and graph analysis. A case study of Hurricane Harvey in 2017 in Houston was conducted to illustrate the application of the proposed approach. The findings highlighted the temporal patterns of critical infrastructure events that occurred in disasters including disruptive events and their adverse impacts on communities. The findings also provided insights for better understanding critical infrastructure interdependencies in disasters. From the practical perspective, the proposed methodology study can improve the ability of community members, first responders and decision makers to detect and respond to infrastructure disruptions in disasters.
Smart City Digital Twins, Decision Analytics, Mobile Services, and Service Science, disasters, graph-based approach, infrastructure disruptions, social media, digital twin
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