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Mining the Web: How user-generated content can become a data source for tourism research

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This is a presentation that I gave at the 2009 Travel and Tourism Research Association of Canada annual meeting.

This is a presentation that I gave at the 2009 Travel and Tourism Research Association of Canada annual meeting.

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Mining the Web: How user-generated content can become a data source for tourism research Mining the Web: How user-generated content can become a data source for tourism research Presentation Transcript

  • Mining the Web how user-generated content (UGC) can become a data source for tourism research Peter A. Johnson and Dr. Renee Sieber, McGill University TTRA Canada Annual Conference, Guelph Ontario Thursday October 15, 2009
  • Outline • What is user generated content (UGC)? • Examples of tourism-related UGC • Tripadvisor study • Challenges to UGC
  • What is UGC?
  • • User-generated content is: • content made publicly available over the Internet • reflects creative effort • created outside of professional routines and practices (OECD, 2007) http://www.oecd.org/dataoecd/57/14/38393115.pdf
  • Tripadvisor study • A popular travel rating site • Determine the range and nature of reviews of Nova Scotia • Start search queries using “nova scotia” and “halifax nova scotia” • Web scrape as many reviews as possible
  • Web Scraping • Specialized computer software (robot or spider) • Automated extraction of website data • Simulates “clicks” to drill down through a web page • Outputs thousands of records in hours
  • 5730 total reviews 5000 4064 3750 2500 1513 1250 153 0 Attractions Restaurants Accommodations Reviews
  • Web Scraping Results
  • Survey vs. UGC Survey UGC Sample Controlled Uncontrolled Type Question Open/Close Generally Type Ended Open-Ended Research Investigative Exploratory Approach
  • 77 Reviewed Locations
  • Accommodation Reviews
  • Attraction Reviews activity
  • attractions Restaurant Reviews
  • Total Destination Review Breakdown Halifax Annapolis Royal Baddeck Lunenburg Dartmouth Yarmouth 33% Digby 40% Other 3% 4% 4% 6% 5% 5%
  • Accommodation Review Ratings One Star Two Stars Three Stars Four Stars 7% Five Stars 8% 10% 53% 22%
  • Attraction Review Ratings One Star Two Stars Three Stars Four Stars 7% 5% Five Stars 9% 56% 23%
  • Restaurant Review Ratings One Star Two Stars Three Stars Four Stars 7% Five Stars 8% 37% 17% 32%
  • Challenges with UGC • Quality varies widely • Vendetta/self promotion • Legal grey area • Generalizability?
  • The Future • Data gathering and analysis: • geolocate reviewers • content analysis of reviews • Secondary UGC: reviews of reviews • Instant feedback: iPhone effect
  • Tripadvisor iPhone Application
  • Yelp iPhone Application
  • Take home points • UGC is an emerging source of data for tourism research • Challenges: • getting and using UGC • how to use results at larger scales
  • Thank You! Further Reading • Girardin, F., Dal Fiore, F., Rattic, C, and Blatt, J. (2008) Leveraging explicitly disclosed location information to understand tourist dynamics: a case study. Journal of Location Based Services 2(1), 41-56. • Goodchild, M.F. (2007). Citizens as Sensors: The World of Volunteered Geography. Geo Journal 69, 211-221. • Gorman S P, (2007), Is academia missing the boat for the Geo Web revolution? A response to Harvey’s commentary. Environment and Planning B: Planning and Design 34(6), 949 – 950 • Haklay, Muki, Alex Singleton and Chris Parker, (2008). Web Mapping 2.0: The Neogeography of the GeoWeb. Geography Compass 2(6), 2011-2039. Contact: peter.johnson2@mail.mcgill.ca