Applied Methodology for Identifying Hurricane-Induced Social Media Signal Changes in Vulnerable Populations

Abstract

Current crisis informatics research using social media to identify human location and behavior has neglected to identify which populations may be prevented from using social media during a disaster. Unfortunately, most crisis informatics focuses on either individual postings or large-scale analyses of signal changes and is potentially overlooking or misrepresenting vulnerable populations. To assess this, we utilize scaled spatial nets and disaggregated population and vulnerability index data in order to identify relationships between social media signal deviation and vulnerable populations within areas that experienced substantial infrastructural damage from Hurricane Harvey. Many studies have identified a strong positive correlation between areas experiencing high amounts of hurricane damage and increased Twitter activity; however, we find that highly damaged areas with more elderly people, disabled people, and people without access to vehicles instead have a significant negative correlation with Twitter activity during disaster. In defining this relationship, we show that some vulnerable populations have decreased instead of increased social media visibility during disasters. These findings identify demographic inequalities in the scalar representation of data from humans-as-sensors in disaster.

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