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Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System

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 7×) and the variety (up to 18×) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity.

Pervasive and Mobile Computing, 2020

External authors: Marco Avvenuti (University of Pisa)
IIT authors:

Salvatore Bellomo

Foto di Salvatore Bellomo

Leonardo Nizzoli

Foto di Leonardo Nizzoli

Type: Contributo in rivista ISI
Field of reference: Computer Science & Engineering

File: Avvenuti, 2020, Towards better social crisis data with HERMES - Hybrid sensing for EmeRgency ManagEment System.pdf

Activity: Social Media Analysis