Web fragmentation - a network analysis approach

Slide 1: University of Ljubljana Faculty of social sciences 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

Slide 2: Internet as a Network • Internet is a network of servers, connected together • WWW is a network of web pages, connected together with hyperlinks Ideal examples for large network analysis

Slide 3: Internet and its’ Users • Marketing and Social research  Internet as a medium  Importance of the users • Audience measurement • Advertising measurement

Slide 4: Internet Activities/Audience Measurement 1) Server-centric approach (Log Analysis) • Limited to “one-site” measurement (usually) • Problems of inference from server data to the behavior of the users 2) User-centric approach • Problems of the recall • Expensive • Intrusive

Slide 5: Cookie-Pixel based technology Server B - Pixel ID: B Server A - Pixel ID: A Server C – Pixel ID: C Central (AD/Measurement) Server Main Cookie-Pixel DATABASE

Slide 6: Web Meta-Data • WEBTRACKER  http://www.ljudmila.org/matej/webtracker/ • Java Script within HTML code with hidden values  Running on users computer (localy) • Data automatically submitted to Central server Database Unknown to user Problems with Java-Script blocking Mozilla • Can be matched with Web Survey data TEST

Slide 7: WWW as Two-Mode Network Set 1 Set 2 Relation: Web Sites Visitors User visiting Web Site Complete Network Web Complete Network of Site Visitors Web Sites Relation: Relation: Visiting common Web Common User of two site Web Sites

Slide 9: Example: WWW.Si Monitor • Data collected in WWW.Si Monitor ( ) • Cookie-Pixel based data collection on 28 Slovenian web sites • 478.920 Different Users • 186.717 Users visited at least two of measured Web Sites within measuring period (28. March – 8. April 2002)

Slide 10: 28 Verticies (Web Sites)

Slide 13: Web sites with more than 3,000 common users

Slide 14: Web sites with more than 5,000 common users

Slide 15: Web sites with more than 6,000 common users

Slide 16: Web sites with more than 12,000 common users

Slide 17: What else should be done? • Collect the data on as much web sites as possible • Match data from different sources Cookie-Pixel Technology (knowledge about Web Sites’ visiting) Meta-Data Survey Data (socio-psychography of Web Sites’ users) • Network model should be developed Depends on problem

Slide 18: Privacy issues • Several dimensions of privacy. • For this case is relevant information privacy, which is a right of individual to keep the data and information about himself private. • Legislation principles for collecting data:  relevancy, notification of use and time storage, compliance of individual. • Not fully applied on the internet. • Privacy on the Internet - An integrated EU Approach to On-line Data Protection - November 2000.