Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2019
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| Acceso en línea: | |
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| _version_ | 1866912152491655168 |
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| author | Avvenuti, Marco Bellomo, Salvatore Cresci, Stefano Nizzoli, Leonardo Tesconi, Maurizio |
| author_facet | Avvenuti, Marco Bellomo, Salvatore Cresci, Stefano Nizzoli, Leonardo Tesconi, Maurizio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1912_02182 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System Avvenuti, Marco Bellomo, Salvatore Cresci, Stefano Nizzoli, Leonardo Tesconi, Maurizio Human-Computer Interaction Computers and Society Machine Learning 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. |
| title | Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System |
| topic | Human-Computer Interaction Computers and Society Machine Learning |
| url | https://arxiv.org/abs/1912.02182 |