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

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

http://toursim.wordpress.com	295
http://geothink.tistory.com	20
http://rose.geog.mcgill.ca	19
http://www.neogeography.kr	9
http://www.slideshare.net	9
http://geothink.textcube.com	2
http://translate.googleusercontent.com	2

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

by Peter Johnson on Nov 15, 2009

Accessibility

Categories

Tags

Upload Details

Usage Rights

7 Embeds 356

Statistics

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