Résumé
"Twitter has emerged as a platform that is heavily used during disasters. Therefore, as an event unfolds, it generates varying levels of online engagement from victims as well as onlookers (both physical and virtual). Because methods for mining disaster-related content at scale must contend with the problem of filtering out vast numbers of unrelated posts, any prior knowledge about the characteristics of disaster-related content in the live Twitter feed may help improve the recovery of relevant posts. In this study, we consider the relative abundance of a disasters Twitter content over time (both relative to total event-related content and relative to the overall volume of content generated on Twitter). We refer to this time-varying abundance as the events signature. In an analysis of three different disasters, we find that event signatures are qualitatively diffrent. These differences can be explained in terms of several characteristics of disasters: foreknowledge, duration, severity, and news media engagement."--Page 1.