Modeling the dynamics of hurricane evacuation decisions from twitter data: An input output hidden markov modeling approach

Abstract Evacuations play a critical role in saving human lives during hurricanes. But individual evacuation decision-making is a complex dynamic process, often studied using post-hurricane survey data. Alternatively, ubiquitous use of social media generates a massive amount of data that can be used to predict evacuation behavior in real time. In this paper, we present a method to infer individual evacuation behaviors (e.g., evacuation decision, timing, destination) from social media data. We develop an input output hidden Markov model (IO-HMM) to infer evacuation decisions from user tweets. To extract the underlying evacuation context from tweets, we first estimate a word2vec model from a corpus of more than 100 million tweets collected over four major hurricanes. Using input variables such as evacuation context, time to landfall, type of evacuation order, and the distance from home, the proposed model infers what activities are made by individuals, when they decide to evacuate, and where they evacuate to. To validate our results, we have created a labeled dataset from 38,256 tweets posted between September 2, 2017 and September 19, 2017 by 2,571 users from Florida during hurricane Irma. Our findings show that the proposed IO-HMM method can be useful for inferring evacuation behavior in real time from social media data. Since traditional surveys are infrequent, costly, and often performed at a post-hurricane period, the proposed approach can be very useful for predicting evacuation demand as a hurricane unfolds in real time.