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Messages posted to social media in the aftermath of a natural disaster are not only useful for detecting the event itself. More importantly, mining such deliberately dropped digital traces allows a precise situational awareness, from which disaster's consequences on population and infrastructures can be timely estimated. Yet, to date, the automatic assessment of damage has received little attention. Here, the authors explore feeding predictive models by tweets conveying on-the-ground social sensors observations to nowcast the perceived intensity of earthquakes.

Nowcasting of Earthquake Consequences using Big Social Data