Nowcasting of Earthquake Consequences using Big Social Data

Abstract

Messages posted to social media in the aftermath of a natural disaster have value beyond detecting the event itself. Mining such deliberately dropped digital traces allows a precise situational awareness, to help provide a timely estimate of the disaster’s consequences on the population and infrastructures. Yet, to date, the automatic assessment of damage has received little attention. Here, the authors explore feeding predictive models by tweets conveying on-theground social sensors’ observations, to nowcast the perceived intensity of earthquakes.