Event Status
Scheduled
Social media has become an essential channel for posting disaster-related information, which provides governments and relief agencies real-time data for better disaster management. However, research in this field has not received sufficient attention, and extracting useful information is still challenging. The work presented aims to improve disaster relief efficiency via mining and analyzing social media data like public attitudes toward disaster response and public demands for targeted relief supplies during different types of disasters using machine learning models. The change of public opinion during different natural disasters and the evolution of peoples’ behavior of using social media for disaster relief in the face of the identical type of natural disasters as Twitter continues to evolve are also studied. The research results demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster management. Learn more.
Sasha (Zhijie) Dong is an Assistant Professor of Industrial Engineering at Texas State University. Her current research focus is on applying and inventing data and computational science methods (e.g., machine learning and optimization) to improve the efficiency, effectiveness and equity of disaster response and crisis management. She is the recipient of NSF CISE CRII Award and has been selected as NSF OSEEER ECM Program Fellow.
Event Details
Date and Time
Oct. 22, 2021, All Day