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  5. TwiFelt: real-time mapping of earthquake perception areas through the analysis of Twitter streams
 
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TwiFelt: real-time mapping of earthquake perception areas through the analysis of Twitter streams

Author(s)
D'Auria, Luca  
Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia  
Giudicepietro, Flora  
Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia  
Sponsors
Istituto Nazionale di Geofisica e Vulcanologia
Language
English
Obiettivo Specifico
4T. Fisica dei terremoti e scenari cosismici
5T. Sorveglianza sismica e operatività post-terremoto
1IT. Reti di monitoraggio e Osservazioni
Status
Published
Peer review journal
Yes
Date Issued
2013
Alternative Location
http://istituto.ingv.it/l-ingv/produzione-scientifica/rapporti-tecnici-ingv/archivio/rapporti-tecnici-2013/2015-02-12.9649518743
Series/Report No.
Rapporti Tecnici INGV
254
URI
https://www.earth-prints.org/handle/2122/9776
Subjects
04. Solid Earth::04.06. Seismology::04.06.06. Surveys, measurements, and monitoring  
05. General::05.03. Educational, History of Science, Public Issues::05.03.99. General or miscellaneous  
Subjects

twitter

earthquake perception...

Abstract
Twitter is one of the most used social networks and its specific features makes it well suited for the real-time analysis of geographic trends of a specific topic. Earle et al. (2011) have shown how the analysis of Twitter streams can provide a useful tool for the early detection of earthquakes at a global scale. They proved that data mining of social networks could provide useful information in Seismology. Here we present a software system named TwiFelt, aimed at providing real-time earthquake perception maps from the analysis of Twitter streams. The system is based on the collection of geotagged tweets (i.e. tweets having a geographic reference) containing selected keywords, its statistical interpretation and its interactive graphical representation.
Type
report
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2013_Dauria.pdf

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Main article
Size

905.97 KB

Format

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Checksum (MD5)

c026b6996eb2b340027f60e43f4a49e4

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