Tourists are an important asset for the economy of the regions they visit. The answer to the question “where do tourists actually go?” could be really useful for public administrators and local governments. In particular, they need to understand what tourists actually visit, where they actually spend nights, and so on and so forth. In this paper, we introduce an original approach that exploits geo-located messages posted by Twitter users through their smartphones when they travel. Tools developed within the FollowMe suite track movements of Twitter users that post tweets in an airport and reconstruct their trips within an observed area. To illustrate the potentiality of our method, we present a simple case study in which trips are traced on the map (through KML layers shown in Google Earth) based on different analysis dimensions.

(2015). Knowledge Discovery from Geo-Located Tweets for Supporting Advanced Big Data Analytics: A Real-Life Experience [conference presentation - intervento a convegno]. Retrieved from http://hdl.handle.net/10446/58374

Knowledge Discovery from Geo-Located Tweets for Supporting Advanced Big Data Analytics: A Real-Life Experience

Psaila, Giuseppe;Toccu, Maurizio Pietro
2015-01-01

Abstract

Tourists are an important asset for the economy of the regions they visit. The answer to the question “where do tourists actually go?” could be really useful for public administrators and local governments. In particular, they need to understand what tourists actually visit, where they actually spend nights, and so on and so forth. In this paper, we introduce an original approach that exploits geo-located messages posted by Twitter users through their smartphones when they travel. Tools developed within the FollowMe suite track movements of Twitter users that post tweets in an airport and reconstruct their trips within an observed area. To illustrate the potentiality of our method, we present a simple case study in which trips are traced on the map (through KML layers shown in Google Earth) based on different analysis dimensions.
2015
Inglese
Model and Data Engineering. 5th International Conference, MEDI 2015, Rhodes, Greece, September 26-28, 2015. Proceedings
Bellatreche, Ladjel; Manolopoulos, Yannis;
978-3-319-23780-0
9344
285
294
cartaceo
online
Springer
MEDI 2015: 5th International Conference, Model and Data Engineering, Rhodes, Greece, 26-28 September 2015
5th
Rhodes (Greece)
26-28 September 2015
internazionale
contributo
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
social networks; trip analysis; knowledge discovery
info:eu-repo/semantics/conferenceObject
3
Cuzzocrea, Alfredo; Psaila, Giuseppe; Toccu, Maurizio Pietro
1.4 Contributi in atti di convegno - Contributions in conference proceedings::1.4.01 Contributi in atti di convegno - Conference presentations
reserved
Non definito
273
(2015). Knowledge Discovery from Geo-Located Tweets for Supporting Advanced Big Data Analytics: A Real-Life Experience [conference presentation - intervento a convegno]. Retrieved from http://hdl.handle.net/10446/58374
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/58374
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