New sources of geotagged information derived from social media like Twitter show great promise for geographic research in tourism. This paper describes an approach to analyze geotagged social media data from Twitter to characterize spatial, temporal and demographic features of tourist flows in Cilento - a regional tourist attraction in southern Italy. It demonstrates how the analysis of geotagged social media data yields more detailed spatial, temporal and demographic information of tourist movements, in comparison to the current understanding of tourist flows in the region. The insights obtained from our case study illustrate the potential of the proposed methodology yet attention should be paid to biases in the data as well as methodological limitations when drawing conclusions from analytical results.

Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy / Chua, Alvin; Servillo, Loris; Marcheggiani, Ernesto; Moere, Andrew Vande. - In: TOURISM MANAGEMENT. - ISSN 0261-5177. - STAMPA. - 57:(2016), pp. 295-310. [10.1016/j.tourman.2016.06.013]

Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy

MARCHEGGIANI, Ernesto;
2016-01-01

Abstract

New sources of geotagged information derived from social media like Twitter show great promise for geographic research in tourism. This paper describes an approach to analyze geotagged social media data from Twitter to characterize spatial, temporal and demographic features of tourist flows in Cilento - a regional tourist attraction in southern Italy. It demonstrates how the analysis of geotagged social media data yields more detailed spatial, temporal and demographic information of tourist movements, in comparison to the current understanding of tourist flows in the region. The insights obtained from our case study illustrate the potential of the proposed methodology yet attention should be paid to biases in the data as well as methodological limitations when drawing conclusions from analytical results.
2016
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/236187
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