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Web fragmentation a network analysis approach Gašper Koren, Matej Kovačič Faculty of Social Sciences, University of Ljublj...
Internet as a Network <ul><li>Internet is a network of servers, connected together </li></ul><ul><li>WWW is a network of w...
Internet and its’ Users <ul><li>Marketing and Social research  </li></ul><ul><li>   Internet as a medium </li></ul><ul><l...
Internet Activities/Audience Measurement <ul><li>1) Server-centric approach (Log Analysis)‏ </li></ul><ul><li>Limited to “...
Cookie-Pixel based technology Central (AD/Measurement)‏ Server Main Cookie-Pixel DATABASE Server B - Pixel ID: B Server C ...
Web Meta-Data <ul><li>WEBTRACKER  </li></ul><ul><li>   http://www.ljudmila.org/matej/webtracker/ </li></ul><ul><li>Java S...
WWW as Two-Mode Network Relation: User visiting Web Site Set 2 Web Sites Set 1 Visitors Complete Network of Web Sites Rela...
 
Example:  WWW.Si Monitor <ul><li>Data collected in WWW.Si Monitor (  )‏ </li></ul><ul><li>Cookie-Pixel based data collecti...
28 Verticies (Web Sites)‏
 
 
Web sites with more than 3,000 common users
Web sites with more than 5,000 common users
Web sites with more than 6,000 common users
Web sites with more than 12,000 common users
What else should be done? <ul><li>Collect the data on as much web sites as possible </li></ul><ul><li>Match data from diff...
Privacy issues <ul><li>Several dimensions of privacy. </li></ul><ul><li>For this case is relevant  information privacy , w...
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Web fragmentation - a network analysis approach

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Presentation at Methodology and Statistics conference, Ljubljana, September 2002

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Web fragmentation - a network analysis approach

  1. 1. Web fragmentation a network analysis approach Gašper Koren, Matej Kovačič Faculty of Social Sciences, University of Ljubljana Zenel Batagelj CATI – Marketing, Media and Social Research & Consulting University of Ljubljana Faculty of social sciences
  2. 2. Internet as a Network <ul><li>Internet is a network of servers, connected together </li></ul><ul><li>WWW is a network of web pages, connected together with hyperlinks </li></ul>Ideal examples for large network analysis
  3. 3. Internet and its’ Users <ul><li>Marketing and Social research </li></ul><ul><li> Internet as a medium </li></ul><ul><li> Importance of the users </li></ul><ul><li>Audience measurement </li></ul><ul><li>Advertising measurement </li></ul>
  4. 4. Internet Activities/Audience Measurement <ul><li>1) Server-centric approach (Log Analysis)‏ </li></ul><ul><li>Limited to “one-site” measurement (usually)‏ </li></ul><ul><li>Problems of inference from server data to the behavior of the users </li></ul>2) User-centric approach <ul><li>Problems of the recall </li></ul><ul><li>Expensive </li></ul><ul><li>Intrusive </li></ul>
  5. 5. Cookie-Pixel based technology Central (AD/Measurement)‏ Server Main Cookie-Pixel DATABASE Server B - Pixel ID: B Server C – Pixel ID: C Server A - Pixel ID: A
  6. 6. Web Meta-Data <ul><li>WEBTRACKER </li></ul><ul><li> http://www.ljudmila.org/matej/webtracker/ </li></ul><ul><li>Java Script within HTML code with hidden values </li></ul><ul><ul><ul><li> Running on users computer (localy)‏ </li></ul></ul></ul><ul><li>Data automatically submitted to Central server Database </li></ul><ul><ul><ul><li>Unknown to user </li></ul></ul></ul><ul><ul><ul><li>Problems with Java-Script blocking </li></ul></ul></ul><ul><ul><ul><li>Mozilla </li></ul></ul></ul><ul><li>Can be matched with Web Survey data </li></ul>TEST
  7. 7. WWW as Two-Mode Network Relation: User visiting Web Site Set 2 Web Sites Set 1 Visitors Complete Network of Web Sites Relation: Common User of two Web Sites Complete Network Web Site Visitors Relation: Visiting common Web site
  8. 9. Example: WWW.Si Monitor <ul><li>Data collected in WWW.Si Monitor ( )‏ </li></ul><ul><li>Cookie-Pixel based data collection on 28 Slovenian web sites </li></ul><ul><li>478.920 Different Users </li></ul><ul><li>186.717 Users visited at least two of measured Web Sites within measuring period (28. March – 8. April 2002)‏ </li></ul>
  9. 10. 28 Verticies (Web Sites)‏
  10. 13. Web sites with more than 3,000 common users
  11. 14. Web sites with more than 5,000 common users
  12. 15. Web sites with more than 6,000 common users
  13. 16. Web sites with more than 12,000 common users
  14. 17. What else should be done? <ul><li>Collect the data on as much web sites as possible </li></ul><ul><li>Match data from different sources </li></ul><ul><ul><li>Cookie-Pixel Technology </li></ul></ul><ul><ul><ul><li>(knowledge about Web Sites’ visiting)‏ </li></ul></ul></ul><ul><ul><li>Meta-Data </li></ul></ul><ul><ul><li>Survey Data </li></ul></ul><ul><ul><ul><li>(socio-psychography of Web Sites’ users)‏ </li></ul></ul></ul><ul><li>Network model should be developed </li></ul><ul><ul><li>Depends on problem </li></ul></ul>
  15. 18. Privacy issues <ul><li>Several dimensions of privacy. </li></ul><ul><li>For this case is relevant information privacy , which is a right of individual to keep the data and information about himself private. </li></ul><ul><li>Legislation principles for collecting data: </li></ul><ul><ul><li> relevancy, notification of use and time storage, compliance of individual. </li></ul></ul><ul><li>Not fully applied on the internet. </li></ul><ul><li>Privacy on the Internet - An integrated EU Approach to On-line Data Protection - November 2000. </li></ul>

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