Name
Abstract | ||
|---|---|---|
Geolocated social media data streams are challenging data sources due to volume, velocity, variety, and unorthodox vocabulary. However, they also are an unrivaled source of eye-witness accounts to establish remote situational awareness. In this paper we summarize some of our approaches to separate relevant information from irrelevant chatter using unsupervised and supervised methods alike. This allows the structuring of requested information as well as the incorporation of unexpected events into a common overview of the situation. A special focus is put on the interplay of algorithms, visualization, and interaction. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-23461-8_14 | ECML/PKDD |
Keywords | Field | DocType |
Stream processing,Machine learning,Social media | Geospatial analysis,Data science,Data stream mining,Data-driven,Situation awareness,Visualization,Computer science,Unexpected events,Structuring,Vocabulary | Conference |
Volume | ISSN | Citations |
9286 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harald Bosch | 1 | 361 | 19.16 |
| Robert Kruger | 2 | 58 | 4.07 |
| Dennis Thom | 3 | 178 | 10.72 |