Social Network Analysis of a Disaster Behavior Network: An Agent-Based Modeling Approach
Disasters are causing tremendous damage to human lives and properties. The United Nations International Strategy for Disaster Reduction (UNISDR) recognizes that behavioral change of society is needed to significantly reduce disaster losses. There is a need therefore in empirical understanding of human behavior during disasters as this could help in making decisions on how to prepare for disasters, how to properly act and strategically respond during and after a calamity. This study aims to understand human behavior during disaster through agent-based modeling and social network analysis. eBayanihan, a disaster management platform that uses crowdsourcing to gather disaster-related information was used to capture disaster behavior during a simulated disaster-event. Survey data was also used for disaster behavior modeling. Generated disaster behavior models and computed social network centrality measures using ORA-Netscenes shows that there are specific agents in the network that can play an important role during disaster risk reduction and management (DRRM) operations.
R. C. Rodrigueza and M. R. J. E. Estuar, "Social Network Analysis of a Disaster Behavior Network: An Agent-Based Modeling Approach," 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, 2018, pp. 1100-1107. doi: 10.1109/ASONAM.2018.8508651