Social media data and housing recovery following extreme natural hazards

Abstract

Identifying initiatives that influence the decision-making process of individuals in the aftermath of extreme natural events is a critical task in post-disaster recovery research. Due to the diversity of disaster-induced physical and psychosocial damage, as well as the complexity of human behavior, a comprehensive understanding of contributing factors requires a collective effort. The growth of social media platforms with millions of users provides researchers with an exceptional opportunity to conceptualize spatial patterns and communal behaviors. This longitudinal study proposes a multistep machine learning algorithm to understand such recovery decisions using social media data. Two publicly available databases, New York City tax lot data and 109 million geotagged tweets from the period October 2012–October 2014 were used to explore residents’ recovery decisions in the two years following Hurricane Sandy. The results reveal that communities with more tweets about social interactions and fewer tweets related to infrastructure and assets were more likely to rebuild rather than relocate.