Artificial intelligence and social media to aid disaster response and management

People increasingly use social media such as Facebook and Twitter during disasters and emergencies. Research studies have demonstrated the usefulness of social media information for a number of humanitarian relief operations ranging from situational awareness to actionable information extraction. Moreover, the use of social media platforms during sudden-onset disasters could potentially bridge the information scarcity issue, especially in the early hours when few other information sources are available. In this work, we analyzed Twitter content (textual messages and images) posted during the recent devastating hurricanes namely Harvey and Maria. We employed state of the art artificial intelligence techniques to process millions of textual messages and images shared on Twitter to understand the types of information available on social media and how emergency response organizations can leverage this information to aid their relief operations. Furthermore, we employed deep neural networks techniques to analyze the imagery content to assess the severity of damage shown in the images. Damage severity assessment is one of the core tasks for many humanitarian organization. To perform data collection and analysis, we employed our Artificial Intelligence for Digital Response (AIDR) technology. AIDR combines human computation and machine learning techniques to train machine learning models specialized to fulfill specific information needs of humanitarian organizations. Many humanitarian organizations such as UN OCHA, UNICEF have used the AIDR technology during many major disasters in the past including the 2015 Nepal earthquake, the 2014 typhoon Hagupit and typhoon Ruby, among others.