Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/9776
AuthorsD'Auria, Luca* 
Giudicepietro, Flora* 
TitleTwiFelt: real-time mapping of earthquake perception areas through the analysis of Twitter streams
Issue Date2013
Series/Report no.Rapporti Tecnici INGV
254
URIhttp://hdl.handle.net/2122/9776
Keywordstwitter
earthquake perception
Subject Classification04. 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 
AbstractTwitter 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.
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