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.
A 'glocal' approach for real-time emergency event detection in Twitter / Salza, Dario; Arnaudo, Edoardo; Blanco, Giacomo; Rossi, Claudio. - ELETTRONICO. - (2022), pp. 570-583. (Intervento presentato al convegno International Conference on Information Systems for Crisis Response and Management tenutosi a Tarbes (FR) nel May 22 - May 25, 2022).
A 'glocal' approach for real-time emergency event detection in Twitter
Arnaudo,Edoardo;
2022
Abstract
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.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2971235