Mining Tourist Behavior: A study of Tourist Sequential Activity Pattern through Location Based Social Networks

TALPUR Anmoila-thesis.pdf - Submitted Version (2MB) | Preview

Talpur, Anmoila (2019) Mining Tourist Behavior: A study of Tourist Sequential Activity Pattern through Location Based Social Networks. Research Master thesis, Victoria University.


Much of the current research in tourism has focused on tourists’ behavior analyses in order to help management constructing effective tourism policies and strategic planning to cater for a diverse range of tourists. Insight into tourist movement and activity patterns is deemed beneficial for the tourism sector in many ways, such as designing better travel packages for tourists, maximizing the tourist activity participation and meeting the tourist demands. Existing works in this field have only focused on finding tourists’ travel trajectories; however, they have not been able to provide comprehensive and complete information about the actual anticipated activities at visited locations. This is probably due to the limitation of traditional data collection and analysis approaches. This research proposes to adopt mobile social media data for effective capturing of tourist activity information and utilizes advanced data mining techniques for extracting valuable insights into tourist behavior. The proposed methods and findings of the study have the potential to support tourism managers and policy makers in making better decisions in tourism destination management.

Item type Thesis (Research Master thesis)
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 1506 Tourism
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords pattern mining; social media; tourism; marketing; tourists; mobility patterns; activity patterns; travel behavior; travel industry; tourist destinations; Location Based Social Network
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login