Spatio-temporal analysis in high resolution of curated tweets associated with the November 2016 wildfire in Haifa (Israel)

For the past decade, Twitter has become a robust platform for distributing messages (tweets) among numerous subscribers worldwide. During and around the occurrence of natural hazards, tweet volumes increase significantly. While Twitter is used for near real-time alerts, processes for extracting reported damage from tweets and resolving their geographical spread in high resolution are still under development. In this study we examine the spatio-temporal distribution of tweets associated with the November 2016 fire, which lasted in Haifa (Isreal) for nearly two days. The acquired tweets were classified and filtered using topic modeling procedure, a portion of them were accurately georeferenced by the Open Street Map and GeoNames gazetteers, and their hyperlocal spatio-temporal patterns were examined. It was found that the tweets’ sentiment (peaks and lows) corresponds to the fire’s occurring cascading events while their spatial distribution can be aligned with most of the actual (true) reports. Despite large uncertainties in the process, results show Twitter can serve in the future as another layer of information to assist decision makers and emergency agencies during and after cascading catastrophes.