Location
Grand Wailea, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2020 12:00 AM
End Date
10-1-2020 12:00 AM
Description
The objective of this study is to propose and test a theoretical framework which integrates the human sentiment reactions on social media in disasters into infrastructure resilience assessment. Infrastructure resilience assessment is important for reducing adverse consequences of infrastructure failures and promoting human well-being in natural disasters. Integrating societal impacts of infrastructure disruptions can enable a better understanding of infrastructure performance in disasters and human capacities under the stress of disruptions. However, the consideration of societal impacts of infrastructure disruptions is limited in existing studies for infrastructure resilience assessment. The reasons are twofold: first, an integrative theoretical framework for connecting the societal impacts to infrastructure resilience is missing; and second, gathering empirical data for capturing societal impacts of disaster disruptions is challenging. This study proposed a theoretical framework to examine the relationship between the societal impacts and infrastructure performance in disasters using social media data. Sentiments of human messages for relevant infrastructure systems are adopted as an indicator of societal impacts of infrastructure disruptions. A case study for electricity and transportation systems in Houston during the 2017 Hurricane Harvey was conducted to illustrate the application of the proposed framework. We find a relation between human sentiment and infrastructure status and validate it by extracting situational information from relevant tweets and official public data. The findings enable a better understanding of societal expectations and collective sentiments regarding the infrastructure disruptions. Practically, the findings also improve the ability of infrastructure management agencies in infrastructure prioritization and planning decisions.
Rethinking Infrastructure Resilience Assessment with Human Sentiment Reactions on Social Media in Disasters
Grand Wailea, Hawaii
The objective of this study is to propose and test a theoretical framework which integrates the human sentiment reactions on social media in disasters into infrastructure resilience assessment. Infrastructure resilience assessment is important for reducing adverse consequences of infrastructure failures and promoting human well-being in natural disasters. Integrating societal impacts of infrastructure disruptions can enable a better understanding of infrastructure performance in disasters and human capacities under the stress of disruptions. However, the consideration of societal impacts of infrastructure disruptions is limited in existing studies for infrastructure resilience assessment. The reasons are twofold: first, an integrative theoretical framework for connecting the societal impacts to infrastructure resilience is missing; and second, gathering empirical data for capturing societal impacts of disaster disruptions is challenging. This study proposed a theoretical framework to examine the relationship between the societal impacts and infrastructure performance in disasters using social media data. Sentiments of human messages for relevant infrastructure systems are adopted as an indicator of societal impacts of infrastructure disruptions. A case study for electricity and transportation systems in Houston during the 2017 Hurricane Harvey was conducted to illustrate the application of the proposed framework. We find a relation between human sentiment and infrastructure status and validate it by extracting situational information from relevant tweets and official public data. The findings enable a better understanding of societal expectations and collective sentiments regarding the infrastructure disruptions. Practically, the findings also improve the ability of infrastructure management agencies in infrastructure prioritization and planning decisions.
https://aisel.aisnet.org/hicss-53/da/digital_twins/3