AI and Big Data in Tourism and Hospitality 2: social media, spatially distributed data and data mining
Type: Virtual Paper
Day: 2/28/2022
Start Time: 3:40 PM
End Time: 5:00 PM
Theme:
Sponsor Group(s):
Recreation, Tourism, and Sport Specialty Group
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Organizer(s):
Andrei Kirilenko
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Chairs(s):
Andrei Kirilenko, University of Florida
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Description:
This session invites innovative advanced data intensive research on tourism-related issues with the goal of exchanging ideas, new approaches, and forming potential collaborations. The data revolution, which started during the past decade, brought new possibilities for decision making and innovation based on the novel methods of analysis of (typically) very large sets of data. Tourism analytics is a new area. Evidentially, the field is highly fragmented, the methods to analyze data are not firmly set, are still evolving and very fluid. We invite submissions in tourism analytics, including but not limiting to the following topics:
• Spatial data analysis and visualization with GIS and other geospatial technologies and models (GPS, RS, LiDAR, digital traces, etc.). This includes mapping of tourist routes, tourist flows, travel photo locations, geo-locations of tweets, emotional mapping, and other spatially distributed social data.
• Analysis of social media (Twitter, Facebook, Instagram and similar platforms), online customer reviews, tourist experiences reported online and other user-generated content.
• Analysis of unstructured data: text analysis, sentiment analysis, analysis of photographs and video.
• People as sensors (digital traces, big data from sensory experiences, Google glasses and similar technologies).
We welcome papers covering data intensive applications in tourism, hospitality, and recreation.
Presentation(s), if applicable
Matan Mor, ; Machine learning for classifying international tourists using big data/social media photos |
Nicole Payntar, University of Texas - Austin; Detecting Unticketed, Emerging Heritage Tourism Attractions in Peru using Machine Learning and Geotagged Photographs |
Junyu Lu, Arizona State University; Spatiotemporal changes in visitation to U.S. national parks and associated social inequity: A big data approach |
Luyu Wang, University of Florida; Investigation of the Spatial Distribution of Tourists Traveling to National Parks Worldwide by Geo-tagged User-generated Content |
Will Payne, Rutgers University - Edward J. Bloustein; Using Local Review Data to Analyze Shifting “Catchment Areas” of Gentrifying Businesses |
Non-Presenting Participants Agenda
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AI and Big Data in Tourism and Hospitality 2: social media, spatially distributed data and data mining
Description
Virtual Paper
Contact the Primary Organizer
Andrei Kirilenko - andrei.kirilenko@ufl.edu
Session Sponsored By