A social computing solution to disaster relief

Abstract

Disaster relief has chronically been a major issue, and various solutions have been presented, attempting to provide the best relief. Currently, disaster rescue teams are facing the problem of lack of valid information at the rescuring scene, resulting in worse relief and more casualties. Data analytics on social network has received success in multiple other application[s] like spam filtering and trend prediction, showing its potential in the field of disaster relief, with a few potential improvements like expanding the size of the dataset and including a more detailed map. The purpose of this research is to expand on previous applications and use social media data to generate a detailed disaster sitution map for first responders. With validated information about the disaster, both survivors and recuers can pinpoint hazardous areas and avoid further damage.