CHS: Medium: Hyperlocal and Hypertemporal Information in Mass Emergencies Events: Next Generation Crisis Informatics Data Collection and Analytics

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This project will advance the empirical study of crisis informatics by moving the extensive analytics developed for post processing of social media-based crisis information to the point of data collection to create a set of intelligent data collection techniques. Crisis informatics is a multidisciplinary field combining human-computer interaction and social computing; its central tenet is that people use personal information and communication technology to respond to disaster in creative ways to cope with uncertainty. This research will reveal how people respond in disasters, both collectively and individually, by examining their digital traces. This will help create better emergency management practice and policy. The research will also advance the delivery of the information people need in disasters by leveraging the vast social media data sets that are produced during them. The safety of people and provision of emergency care as a function of improved situational awareness are the direct benefits of conducting this research.Past research shows that people local to an event seek and provide information online to help themselves and each other, and that this information is highly localized and temporalized when compared to the global conversation that otherwise arises. Current techniques to derive useful information from the social media stream are limited because they usually cannot scale to the vast growth in social media production; they are often restricted to examining only the language of social media posts and not their metadata; and social media are often collected in ways that prohibit the kinds of analyses that are needed for deep exploration and re-use of the data. This research relies on the body of work that the investigators have empirically conducted on the nature of social media behavior in disasters, and incorporates those rigorous deductive and inductive analytical methods at the point of data collection of the social media streams. This will enable intelligent data collection that can perform real-time analysis that bore in on the hypertemporal and hyperlocal features of information communicated via social media. With agility and rigor, solutions developed in this research will be able to pursue multiple threads of emergent concern that will spawn multiple simultaneous data collection trajectories. This will better enable real-time analysis and visualization of what is happening in the social media record during disaster events as they unfold.