Connected communities improve hazard response: An agent-based model of social media behaviors during hurricanes

Abstract Social media platforms have a developing role in how people respond to hazards, providing a network to seek help and respond to help requests. Understanding the dynamics of behavior on social media networks can improve community-level hazard response efforts. People who experience damages may use social media to seek immediate help for debris removal, supplies delivery or emergency rescue, and peers connected on social media may respond by reposting the help request or providing help in person. This research develops an agent-based model (ABM) to simulate a community of individuals that use social media to seek help and respond to requests for help during a hurricane. Agents represent individuals that are in a community affected by a hurricane and share a social media network. Behavioral rules for seeking help and providing help are developed using the Theory of Planned Behavior and parametrized through analysis of a survey of social media use conducted in communities that were affected by 2018 Hurricanes Florence and Michael. The ABM simulates agents that post help requests, repost help requests, provide help in person, and receive help. A Design of Experiments approach is applied to assess how ABM parameters, including community size, connectivity of a network, damage rate, and propensity for using social media, influence the number of requests for help that are met through the social media network. Results demonstrate that high connectivity leads to rapid reposting and results in cascading responses to meet requests for help.

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