Ubiquitous social media during crises provides citizen reports on the situation, needs and supplies. Previous research extracts resource needs directly from the text (e.g. "Power cut to Coney Island and Brighton beach" indicates a power need). This approach assumes that citizens derive and write about specific needs from their observations, properly specified for the emergency response system, an assumption that is not consistent with general conversational behavior. In our study, Twitter messages (tweets) from Hurricane Sandy in 2012 clearly indicate power blackouts, but not their probable implications (e.g. loss of power to hospital life support systems). We use a domain model to capture such interdependencies between resources and needs. We represent these dependencies in an ontology that specifies the functional association between resources. Accurate interpretation of resource need/supply also depends on the location of a message. We show how inference based on a domain model combined with location detection and interpretation in the social data can enhance situational awareness, e.g., predicting a medical emergency before it is reported as critical.
Available at: http://works.bepress.com/john_flach/155/