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Title: Location intelligence for tourism destinations: a big data comparative analysis through location-based social networks

Authors: Konstantinos Vassakis; Emmanuel Petrakis; Ioannis Kopanakis; John Makridis

Addresses: Management Science and Technology, Hellenic Mediterranean University, Agios Nikolaos, Crete, 72100, Greece ' Department of Economics, University of Crete, Rethymno, Crete, 74100, Greece ' Management Science and Technology, Hellenic Mediterranean University, Agios Nikolaos, Crete, 72100, Greece ' Management Science and Technology, Hellenic Mediterranean University, Agios Nikolaos, Crete, 72100, Greece

Abstract: The existing practical research that social media and location intelligence in tourism is utilising a part of the possibilities that big data analytics can offer. Therefore, innovative big data analytics applications can provide new knowledge about behavioural data and perceptions in tourism destinations. This research develops an innovative approach of leveraging geotagged user-generated content in location-based social networks (LBSNs) for tourism destinations. In contrast to the conventional spatio-temporal analysis, valuable knowledge is extracted about travellers' behaviour, experiences, and opinions for tourist destinations. Our approach's contribution has been demonstrated using user-generated content for the two largest islands in the Mediterranean sea, Crete and Cyprus. The results of our study provide significant insights about the characteristics of the visitors in specific spots, their preferences, opinions and their tempo-spatial movements in tourism destinations - offering valuable information to tourism stakeholders for instant and effective strategic decision making that can lead to innovation and value creation.

Keywords: social networks; big data analytics; location; tourism destinations; LBSNs; location-based social networks; management; location intelligence.

DOI: 10.1504/IJTP.2021.118664

International Journal of Tourism Policy, 2021 Vol.11 No.3, pp.247 - 264

Received: 12 Nov 2020
Accepted: 04 Apr 2021

Published online: 28 Oct 2021 *

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