Name
Title | ||
|---|---|---|
Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System |
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 7×) and the variety (up to 18×) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.pmcj.2020.101225 | Pervasive and Mobile Computing |
Keywords | DocType | Volume |
Human-as-a-sensor,Hybrid sensing,Artificial intelligence,Emergency management,Online social networks | Journal | 67 |
ISSN | Citations | PageRank |
1574-1192 | 0 | 0.34 |
References | Authors | |
30 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Avvenuti | 1 | 37 | 4.91 |
| Bellomo Salvatore | 2 | 0 | 0.34 |
| Cresci, S. | 3 | 235 | 21.79 |
| Nizzoli Leonardo | 4 | 0 | 0.68 |
| Maurizio Tesconi | 5 | 281 | 32.06 |