The technological revolution and the widespread of social media have allowed people to generate tremendous amounts of data every day. Social networks provide users with access to information. This paper aims to determine the attractiveness of various tourism sites by investigating the behaviour of users through social media. The database involves geo-tagged photos located in six cities serving as a relevant artistic and cultural hub in Italy. Photos downloaded from Flickr, a data-sharing platform. Data analysis was conducted using Mathematica and Machine Learning models approach. The results of our study show maps of the users’ behaviour identify the annual trend of photographic activity in cities and highlight the effectiveness of the proposed methodology that is able to provide with place and user information. The study underline how the analysis of social data can to create a predictive model to formulate tourism scenarios. At the end, general tourism marketing strategies are discussed.

Using social media to identify tourism attractiveness in six Italian cities

Giglio, Simona;Bertacchini, Francesca;Bilotta, Eleonora;Pantano, Pietro
2019-01-01

Abstract

The technological revolution and the widespread of social media have allowed people to generate tremendous amounts of data every day. Social networks provide users with access to information. This paper aims to determine the attractiveness of various tourism sites by investigating the behaviour of users through social media. The database involves geo-tagged photos located in six cities serving as a relevant artistic and cultural hub in Italy. Photos downloaded from Flickr, a data-sharing platform. Data analysis was conducted using Mathematica and Machine Learning models approach. The results of our study show maps of the users’ behaviour identify the annual trend of photographic activity in cities and highlight the effectiveness of the proposed methodology that is able to provide with place and user information. The study underline how the analysis of social data can to create a predictive model to formulate tourism scenarios. At the end, general tourism marketing strategies are discussed.
2019
Machine Learning
social media
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/288763
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