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

In: Information and Communication Technologies in Tourism 2022

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zehui Wang & Luca Koroll & Wolfram Höpken & Matthias Fuchs, 2022. "Analysis of Instagram Users’ Movement Pattern by Cluster Analysis and Association Rule Mining," Springer Books, in: Jason L. Stienmetz & Berta Ferrer-Rosell & David Massimo (ed.), Information and Communication Technologies in Tourism 2022, pages 97-109, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-94751-4_10
    DOI: 10.1007/978-3-030-94751-4_10
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