Name
Abstract | ||
|---|---|---|
Recent disasters demonstrated the central role of social media during emergencies thus motivating the exploitation of such data for crisis mapping. We propose a crisis mapping system that addresses limitations of current state-of-the-art approaches by analyzing the textual content of disaster reports from a twofold perspective. A damage detection component employs a SVM classifier to detect mentions of damage among emergency reports. A novel geoparsing technique is proposed and used to perform message geolocation. We report on a case study to show how the information extracted through damage detection and message geolocation can be combined to produce accurate crisis maps. Our crisis maps clearly detect both highly and lightly damaged areas, thus opening up the possibility to prioritize rescue efforts where they are most needed. |
Year | Venue | Field |
|---|---|---|
2015 | WISE | Data science,Data mining,Internet privacy,Social media,Social media mining,Computer science,Geolocation,Emergency management,Crisis mapping,Geoparsing,Natural disaster,Svm classifier |
DocType | Citations | PageRank |
Conference | 13 | 0.67 |
References | Authors | |
15 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cresci, S. | 1 | 235 | 21.79 |
| Andrea Cimino | 2 | 52 | 9.96 |
| Felice Dell'Orletta | 3 | 207 | 34.46 |
| Maurizio Tesconi | 4 | 281 | 32.06 |