Spatio-temporal mining of keywords for social media cross-social crawling of emergency events

Abstract

Being able to automatically extract as much relevant posts as possible from social media in a timely manner is key in many activities, for example to provide useful information in order to rapidly create crisis maps during emergency events. While most social media support keyword-based searches, the amount and the accuracy of retrieved posts depend largely on the keywords employed. The goal of the proposed methodology is to dynamically extract relevant keywords for searching social media during an emergency event, following the event’s evolution. Starting from a set of keywords designed for the type of event being considered (floods and earthquakes, in particular), the set of keywords is automatically adjusted taking into account the spatio-temporal features of the monitored event. The goal is to retrieve posts following the event’s evolution and to benefit from cross-social crawling in order to exploit the specific characteristics of a social media over others. In the case considered in this paper, we exploit the precision of the geolocation of images posted in Flickr to extract keywords to search YouTube posts for the same event, since YouTube does not allow spatial crawling yet provides a richer source of information. The methodology was evaluated on three recent major emergency events, demonstrating a large increase in the number of retrieved posts compared with the use of generic seed keywords. This is a relevant improvement of relevance for providing information on emergency events, and the ability to follow the event’s development.