Social Media Crowdsourcing for Earthquake Damage Assessment

Rapid damage assessment following natural hazard events is critical to first responders, cognizant government agencies, and private organizations. This paper describes the use of crowdsourced social media data to complement more traditional sources of information in monitoring natural hazards and their effects. In a series of studies, the use of social media data, principally Twitter postings, has been investigated as a complementary source of information to more traditional sensor-based systems. The intent is to explore the value of social media data to provide rapid indications of damage following sudden-onset events. The study focused on testing whether social media users react to natural hazard events such that the resulting data can be used to make damage assessments. Text-based assessment and classification models were applied to identify damage levels from contemporary tweets. The study defined a damage assessment scale for earthquake damages adjusted from Modified Mercalli Intensity and developed a text classification model for assessing damages. The results were validated against USGS appraisals of the same event.