Nowcasting earthquake damages with Twitter

Abstract

The Modified Mercalli intensity scale (Mercalli scale for short) is a qualitative measure used to express the perceived intensity of an earthquake in terms of damages. Accurate intensity reports are vital to estimate the type of emergency response required for a particular earthquake. In addition, Mercalli scale reports are needed to estimate the possible consequences of strong earthquakes in the future, based on the effects of previous events. Emergency offices and seismological agencies worldwide are in charge of producing Mercalli scale reports for each affected location after an earthquake. However, this task relies heavily on human observers in the affected locations, who are not always available or accurate. Consequently, Mercalli scale reports may take up to hours or even days to be published after an earthquake. We address this problem by proposing a method for early prediction of spatial Mercalli scale reports based on people’s reactions to earthquakes in social networks. By tracking users’ comments about real-time earthquakes, we create a collection of Mercalli scale point estimates at municipality (i.e., state subdivisions) level granularity. We introduce the concept of reinforced Mercalli support, which combines Mercalli scale point estimates with locally supported data (named ‘local support’). We use this concept to provide Mercalli scale estimates for real-world events by providing smooth point estimates using a spatial smoother that incorporates the distribution of municipalities in each affected region. Our method is the first method based on social media that can provide spatial reports of damages in the Mercalli intensity scale. Experimental results show that our method is accurate and provides early spatial Mercalli reports 30 minutes after an earthquake. Furthermore, we show that our method performs well for earthquake spatial detection and maximum intensity prediction tasks. Our findings indicate that social media is a valuable source of spatial information for quickly estimating earthquake damages.