test Browse by Author Names Browse by Titles of Works Browse by Subjects of Works Browse by Issue Dates of Works
       

Advanced Search
Home   
 
Browse   
Communities
& Collections
  
Issue Date   
Author   
Title   
Subject   
 
Sign on to:   
Receive email
updates
  
My Account
authorized users
  
Edit Profile   
 
Help   
About T-Space   

T-Space at The University of Toronto Libraries >
Journal of Medical Internet Research >
Volume 5 (2003) >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/4681


Title: Searching for Cancer Information on the Internet: Analyzing Natural Language Search Queries
Authors: Bader, Judith L
Theofanos, Mary Frances
Keywords: Original Paper
Cancer
Internet
search engines
natural language processing
Issue Date: 11-Dec-2003
Publisher: Gunther Eysenbach; Centre for Global eHealth Innovation, Toronto, Canada
Citation: Judith L Bader, Mary Frances Theofanos. Searching for Cancer Information on the Internet: Analyzing Natural Language Search Queries. J Med Internet Res 2003;5(4):e31 <URL: http://www.jmir.org/2003/4/e31/>
Abstract: [This item is a preserved copy and is not necessarily the most recent version. To view the current item, visit http://www.jmir.org/2003/4/e31/ ] Background: Searching for health information is one of the most-common tasks performed by Internet users. Many users begin searching on popular search engines rather than on prominent health information sites. We know that many visitors to our (National Cancer Institute) Web site, cancer.gov, arrive via links in search engine result. Objective: To learn more about the specific needs of our general-public users, we wanted to understand what lay users really wanted to know about cancer, how they phrased their questions, and how much detail they used. Methods: The National Cancer Institute partnered with AskJeeves, Inc to develop a methodology to capture, sample, and analyze 3 months of cancer-related queries on the Ask.com Web site, a prominent United States consumer search engine, which receives over 35 million queries per week. Using a benchmark set of 500 terms and word roots supplied by the National Cancer Institute, AskJeeves identified a test sample of cancer queries for 1 week in August 2001. From these 500 terms only 37 appeared ≥ 5 times/day over the trial test week in 17208 queries. Using these 37 terms, 204165 instances of cancer queries were found in the Ask.com query logs for the actual test period of June-August 2001. Of these, 7500 individual user questions were randomly selected for detailed analysis and assigned to appropriate categories. The exact language of sample queries is presented. Results: Considering multiples of the same questions, the sample of 7500 individual user queries represented 76077 queries (37% of the total 3-month pool). Overall 78.37% of sampled Cancer queries asked about 14 specific cancer types. Within each cancer type, queries were sorted into appropriate subcategories including at least the following: General Information, Symptoms, Diagnosis and Testing, Treatment, Statistics, Definition, and Cause/Risk/Link. The most-common specific cancer types mentioned in queries were Digestive/Gastrointestinal/Bowel (15.0%), Breast (11.7%), Skin (11.3%), and Genitourinary (10.5%). Additional subcategories of queries about specific cancer types varied, depending on user input. Queries that were not specific to a cancer type were also tracked and categorized. Conclusions: Natural-language searching affords users the opportunity to fully express their information needs and can aid users naïve to the content and vocabulary. The specific queries analyzed for this study reflect news and research studies reported during the study dates and would surely change with different study dates. Analyzing queries from search engines represents one way of knowing what kinds of content to provide to users of a given Web site. Users ask questions using whole sentences and keywords, often misspelling words. Providing the option for natural-language searching does not obviate the need for good information architecture, usability engineering, and user testing in order to optimize user experience.
Description: Reviewer: Kiley, Robert
Reviewer: Zeng, Q
Reviewer: McCaul, Kevin
URI: http://hdl.handle.net/1807/4681
ISSN: 1438-8871
Other Identifiers: doi:10.2196/jmir.5.4.e31
Rights: Copyright (cc) Retained by author(s) under a Creative Commons License: http://creativecommons.org/licenses/by/2.0/
Appears in Collections:Volume 5 (2003)

Files in This Item:

File Description SizeFormat
jmir.html83.93 kBHTMLView/Open

Items in T-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

uoft