1.1
1
xml
info:srw/schema/1/mods-v3.2
A 'Glocal' Approach for Real-time Emergency Event Detection in Twitter
Dario Salza
author
Edoardo Arnaudo
author
Giacomo Blanco
author
Claudio Rossi
author
2022
Tarbes, France
English
Social media like Twitter offer not only an unprecedented amount of user-generated content covering developing emergencies but also act as a collector of news produced by heterogeneous sources, including big and small media companies as well as public authorities. However, this volume, velocity, and variety of data constitute the main value and, at the same time, the key challenge to implement and automatic detection and tracking of independent emergency events from the real-time stream of tweets. Leveraging online clustering and considering both textual and geographical features, we propose, implement, and evaluate an algorithm to automatically detect emergency events applying a glocal approach, i.e., offering a global coverage while detecting events at local (municipality level) scale.
Emergency
Event Detection
Social Media
Twitter
Incremental Clustering
exported from refbase (http://idl.iscram.org/show.php?record=2440), last updated on Thu, 03 Nov 2022 22:00:10 +0100
text
http://idl.iscram.org/files/dariosalza/2022/2440_DarioSalza_etal2022.pdf
DarioSalza_etal2022
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management
Iscram 2022
Rob Grace
editor
Hossein Baharmand
editor
2022
Tarbes, France
conference publication
570
583
978-82-8427-099-9
2411-3387
1