Seeding Strategies in Online Social Networks for Improving Information Dissemination of Built Environment Disruptions in Disasters

The objective of this study is to propose a seed-search algorithm and develop seeding strategies in online social networks for disseminating credible situational information regarding built environment disruptions in disasters. Rapid and extensive dissemination of credible situational information is important for disaster preparedness, response, and recovery in communities. Online social networks such as Twitter have become popular media sources among the public to share information in disasters. Due to the directed relations and fragmentations in networks, however, little is known about the ways of selecting starting nodes (a.k.a., seeds) in order to broadly and rapidly spread information online. To address this gap, this study proposes a computational approach, which is an integration of greedy algorithm and graph analysis, to capture fragmentations, identify critical seeds, and develop seeding strategies to disseminate information in online social networks. A case study of an infrastructure disruption (water release from reservoirs in Houston) during 2017 Hurricane Harvey was used to illustrate the capabilities of the proposed approach. The results indicate that seeding top 10 users is effective for distributing information to more than 80% of nodes in a network. The findings inform about strategies to better report, transmit, and gather situational information on social media, which can further enhance situation awareness and community resilience in disasters.