This project utilizes information gleaned from social media about upcoming events to inform designated authorities in a timely manner so they can prepare mitigating action plans in case of emergency. Besides the extracted events themselves, harvested information may include (but is not limited to) images, posted messages, people’s sentiments and other surrounding context which will improve relevancy and trust of the information in understanding emergency situations. Extracted events become the source for investigating and analyzing spatial-temporal influences between events and cross-domain events to derive further insights. Potential applications are real-time tracking and monitoring of events for disaster relief, and forecasting of events for mitigation plans. Project outcomes will benefit researchers in information extraction and integration with interests in graph models and transfer learning; in addition to providing practical studying materials in areas such as deep learning, spatio-temporal data causality and analysis for students about disaster resilience and progressing towards community resilience in the long term. Moreover, the work will increase research capacity and collaborations to generate new research opportunities for students from underrepresented communities to pursue advanced degrees in computer science.

Source of funding: NSF