Detecting crisis events from unstructured text data using signal words as crisis determinants

Abstract

Earth observation data provides valuable information and support along the disaster management cycle. However, information from satellite remote sensing is often not available in the first hours a crisis occurs, due to several reasons, e.g. pre-defined acquisition times, cloud coverage, downlink capacities. To fill this time gap and add value to the incoming results from remote sensing data, ancillary datasets such as Twitter data become useful to enrich data and get insights into events by leveraging their spatio-temporal and thematic references. However, the main disadvantage of using Twitter data is the noise that is introduced into analyses by these data. Among other reasons, this is mainly caused by the use of insignificant search criteria that are used to harvest the data, that often result in irrelevant, noisy data (e.g. using insignificant keywords or incorrect geotags to filter data). This paper presents a method to identify crisis-event specific signal words, that are then used together with Part Of Speech (POS) tagging to filter the Twitter streams, and gather crisis-event specific data. These data are then used to estimate the location hotspots of the crisis events. The developed methods are applied as a proof-of-concept to determine flood events in May of 2022.