The predictive relationship between earthquake intensity and tweets rate for real-time ground-motion estimation

ABSTRACT The standard measure for evaluation of the immediate effects of an earthquake on people and man‐made structures is intensity. Intensity estimates are widely used for emergency response, loss estimation, and distribution of public information after earthquake occurrence (Wood and Neumann, 1931; Brazee, 1976). Modern intensity assessment procedures process a variety of information sources. Those sources are primarily from two main categories: physical sensors (seismographs and accelerometers) and social sensors (witness reports). Acquiring new data sources in the second category can help to speed up the existing procedures for intensity calculations. One potentially important data source in this category is the widespread microblogging platform Twitter, ranked ninth worldwide as of January 2016 by number of active users, ∼320 million (Twitter, 2016). In our previous studies, empirical relationships between tweet rate and observed modified Mercalli intensity (MMI) were developed using data from the M  6.0 South Napa, California, earthquake (Napa earthquake) that occurred on 24 August 2014 (Kropivnitskaya et al. , 2016). These relationships allow us to stream data from social sensors, supplementing data from other sensors to produce more accurate real‐time intensity maps. In this study, we validate empirical relationships between tweet rate and observed MMI using new data sets from earthquakes that occurred in California, Japan, and Chile during March–April 2014. The statistical complexity of the validation test and calibration process is complicated by the fact that the Twitter data stream is limited for open public access, reducing the number of available tweets. In addition, in this analysis only spatially limited positive tweets (marked as a tweet about the earthquake) are incorporated into the analysis, further limiting the data set and restricting our study to a historical data set. In this work, the predictive relationship for California is recalibrated slightly, and a new set of relationships is estimated for Japan and Chile.