People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid sensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to ) and the variety (up to ) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity
Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System
Marco Avvenuti;Leonardo Nizzoli;
2020-01-01
Abstract
People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid sensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to ) and the variety (up to ) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularityI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
