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This is the dataset associated with the paper titled: Understanding Hurricane Evacuation Behavior from Facebook Data 

The project combines heterogeneous data sources from transportation systems and social media, in a unified framework-providing better information for modeling dynamic population behavior during hurricanes. 

Evacuations have a significant impact on saving human lives during hurricanes. However, as a complex dynamic process, it is typically difficult to know individual evacuation decisions in real-time. Since a large amount of information is continuously posted through social media platforms, we can use them to understand individual evacuation behavior. In this paper, we collect tweets during Hurricane Irma in 2017 and train a text classifier in an active learning way to distinguish tweets expressing positive evacuation decisions from both negative and irrelevant ones. Additionally, we perform a demographic analysis and content clustering to investigate the potential causes and correlates of evacuation decisions. The results can be used to help inform planning strategies of emergency response agencies. 

Governments' use of social media during all phases of emergency management, especially during disasters, has increased dramatically in the past 20 years. Yet, implementation at the local government level in the United States remains haphazard. As technology and the role of social media evolve, there persists a need to understand the socio‐technical aspects of social media's employment in times of disaster. This study contributes to the growing social media literature by asking the following questions: what challenges remain and what lessons learned are being institutionalised at the local level of government? A qualitative analysis of 26 after action reports on Hurricane Irma (September 2017) by county, state, and federal governments and a four‐hour focus‐group session revealed dominant and subdominant themes, including: push/pull information; capacity and technical issues; inconsistent messaging; one‐way versus two‐way communication; timing of messages; and data collection. The paper concludes by discussing lessons learned, remaining challenges, evidence of organisational learning, and recommendations for future research.