1. Query trends CS 349 Presentation December 2 nd, 2008 Catherine Grevet

2. Introduction to query trend analysis  What is a query trend?  Analysis of large query volumes over time  Queries are organized into similar topics 2

3. How do queries change over time?  Hourly Analysis of a Very Large Topically Categorized Web Query Log [Beitzel et al. 2004]  More queries issues during peak hours than non-peak hours  Some topics are more popular during specific times of the day while others are constant throughout the day.  Absolute change vs. Percentage change 3

4. Google trends  Started around 2006  Search Volume Index and News Reference Volume  Inaccuracies: data sampling issues and approximations used to compute the trends  Hot trends shows hourly popular searches  Searches that deviate the most from their historic trends  Only shows top 100  Displays trends in relative mode  http://www.google.com/trends http://www.google.com/trends 4

5. Example of trend analysis for public health  Study by Eysenbach in 2006  Query trends for syndromic surveillance to signal an outbreak  Google AdSense ads to track geographic locations and number of hits for flu-related queries  Results:  Search engine clicks were better and timelier predictor of flu outbreaks than predicted by physicians  “Infodemiology”: track health demand and supply trends 5

6. Google as a health monitor  November 2008 started flu trends on google.org  http://www.google.org/flutrends/ http://www.google.org/flutrends/ 6

7. Wellesley trends  Wellesley.edu uses a google mini search for searching the wellesley domain  Outputs monthly results for 100 top queries  Data:  100 top queries from November 2006 to September 2008  Goal:  Make sense of the data to improve the wellesley.edu website  How:  Wellesley trends! 7

8. Wellesley trends 8

9. Concerns  Privacy  Needs human labor for categorizing  Inaccuracies and data sampling issues 9

10. To solve the human categorizing problem 10  Goal: understand intent of user through classification  Automatic categorization into informational, navigational and transactional groups [Jansen 2008]  Manually derived the characteristics and then ran the algorithm on search engine logs  This categorization was 74% accurate

11. Conclusions 11  Analyzing query trends can be particularly useful for the next generation of search engines.  Understanding these trends can also have important implications for health, politics…  For domain-specific search engines (such as Wellesley mini search), the queries will be mainly related to the website. By using automatic categorization and analysis of historical trends, the homepage of the website could adapt to the popular queries.

12. References 12  S. Beitzel et al., Hourly Analysis of a Very Large Topically Categorized Web Query Log, SIGIR 2004, 2004  G. Eysenbach, Infodemiology: Tracking Flu-Related Searches on the Web for Syndromic Surveillance, AMIA 2006 Symposium Proceedings, 2006  B. Jansen, Determining the User Intent of Web Search Engine Queries, 2007  http://www.google.com/trends http://www.google.com/trends  Thanks to Claire Lorenz for Wellesley mini search data