Coastal Hurricane Prediction and Communication Dynamics in the Modern Information Environment
We will present the project paradigm and initial results, with an emphasis on empirical analysis of data from social media streams collected during Hurricane Sandy. Research has considered how the use of social media, and specifically Twitter, by members of the public before, during and after a disaster can be used opportunistically to detect the presence of a hazard and derive situational status information about hazardous impacts. The methods for such derivation, however, are being outstripped by the exponential adoption of Twitter, especially during disasters. The volume of Twitter posts in anticipation of hazards like hurricanes that hit highly populated coastal areas is so vast (and growing exponentially) that even advanced human- and machine- techniques cannot sift through the global response to this event to find the “signal in the noise.” As a result, techniques that rely on either human or machine language detection will fail at isolating critical pieces of information that researchers and practitioners need. In this research, we are developing new techniques for detection of protective-decision-making activity by geographically vulnerable Twitterers. By examining movement over time, we can detect those who evacuated and those who sheltered-in-place, and how well their public social media activity aligns with their actions. From these “found” users, we can then perform empirical content analysis of their tweet activity to investigate socio-linguistic behaviors with respect to information use and protective decision-making.