Applying Natural Language Processing to Understand Emergency Communication in Florida Communities during Hurricane Irma

To examine emergency communication on Twitter across different phases of Hurricane Irma, we developed a Python script to access the Twitter Premium application programming interface (API). We applied a stratified sampling method to build a representative sample of 1,168,484 Irma-related tweets posted from August 30, 2017 to September 28, 2017. Using natural language processing (NLP) algorithms, we categorized tweet topics and traced the evolution of communication patterns in affected Florida communities. Our analysis validated established temporal markers for hurricane phases, such as the issuance of watches and the timing of landfall. We further compared emergency communication patterns between counties that were more directly impacted by Hurricane Irma and those outside its path, highlighting significant geographic variations. The results underscore the importance of phase- and location-specific messaging and suggest that emergency management agencies should adopt a more proactive communication approach during the pre-event stage and immediately following landfall.