Analysis of Instagram Users’ Movement Pattern by Cluster Analysis and Association Rule Mining

Zehui Wang (), Luca Koroll (), Wolfram Höpken () and Matthias Fuchs ()
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Zehui Wang: University of Applied Science Ravensburg-Weingarten
Luca Koroll: University of Applied Science Ravensburg-Weingarten
Wolfram Höpken: University of Applied Science Ravensburg-Weingarten
Matthias Fuchs: Mid-Sweden University

A chapter in Information and Communication Technologies in Tourism 2022, 2022, pp 97-109 from Springer

Abstract: Abstract Understanding the characteristics of tourists’ movements is essential for tourism destination management. With advances in information and communication technology, more and more people are willing to upload photos and videos to various social media platforms while traveling. These openly available media data is gaining increasing attention in the field of movement pattern mining as a new data source. In this study, uploaded images and their geographic information within Lake Constance region, Germany were collected and through clustering analysis, a state-of-the-art k-means with noise removal algorithm was compared with the commonly used DBCSCAN on Instagram dataset. Finally, association rules between popular attractions at region-level and city-level were mined respectively. Results show that social media data like Instagram constitute a valuable input to analyse tourists’ movement patterns as input to decision support and destination management.

Keywords: Movement pattern; Big data; Instagram; Crawling; DBSCAN; NK-MEANS; Association rule mining (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-030-94751-4_10

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