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 7x) and the variety (up to 18x) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity.
@article{arxiv.1912.02182,
title = {Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System},
author = {Marco Avvenuti and Salvatore Bellomo and Stefano Cresci and Leonardo Nizzoli and Maurizio Tesconi},
journal= {arXiv preprint arXiv:1912.02182},
year = {2024}
}
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Postprint of the article published in the Pervasive and Mobile Computing journal. Please, cite accordingly