1. Tagging Systems and Their Effect on Resource Popularity Austin Wester

2. Background & Related Work

3.  Tag Purposes  Social bookmarking  Personal bookmarks  Store and retrieve resources  Social tagging systems  Shared tags for particular resources  Each tag is a link to additional resources tagged the same way by other users Background & Related Work

4.  Taxonomy of Tagging Systems  System design and attributes  How the characteristics of a tagging system effects the content, the tags and the usage  Users  How their incentives and motivations affect the tagging system Background & Related Work

5. Methodology

6. Gathering the Information  Visual Studio.Net 2005  Flickr API  Write a program to query Flickr.com  Challenges  Allowed just under 1 query/second (55/min)  Gives 72000 images/day or 2.1 million in a month

7. Converting Information  Write script to separate data into multiple bipartite networks in Pajek format

8. Converting Data  Image/tags by a few different categories  Separating into categories will be more accurate  Possibly separate categories into popular, neutral and unpopular  Image/Comments  Owners/Images  Owners/Comments  This will give me many bipartite graphs to perform several different studies

9. Analyzing The Data

10.  New images will naturally have low number of views and will probably be removed from the study  Flickr has a ‘Most Interesting’ section. I believe these are new images that are receiving a larger number of views than most  These can be analyzed to see if they have tags or not and if they have an affect on the number of views an image is receiving.

11. Analyzing The Data  Use Pajek, VS.net 2005

12. Analyzing The Data  Find Degree, Betweenness and Centrality  Of Images for each network  How many tags an image has  Of Tags for each network  Will tell which tags are used most often  Of Owners for each network  Tell how connected they are  Does this make a difference

13. Analyzing The Data  Find Coefficiency  Image/Tags: see if images with a higher coefficiency are more popular  Image/Comments: see if images that are commented on more are more popular  And so on

14. Analyzing The Data  Convert bipartite graphs into 1-mode

15. Analyzing The Data  An image’s metadata contains number of views and number of favorites  The image’s popularity will be categorized based on a simple calculation. The number of favorites/number of views.  Popular is the top 33% or > 66%  Neutral is > 33% and 33% and < 67%  Unpopular is <= 33%

16. Questions To Be Answered  Owner to Images  Broken down by owner popularity  See if users of high ranking has more popular images than users with low ranking

17. Questions To Be Answered  Owners to comments  See if the number of comments left by users on an owners profile is related to their popularity  If so then check to see if the popularity of the users who left comments plays a role.

18. Questions To Be Answered  Owners to tags  Find out if there is any relation in a user’s popularity based on the tags for their galleries  Will do similar test and comparisons as before

19. Questions To Be Answered  Owners to Owners  Does a user’s friend’s popularity affect their popularity?  Try to compare those with mostly popular friends to those with mostly unpopular (mostly non-active) friends

20. Questions To Be Answered  Images to comments  Similar to Owners to comments  Do the comments left play a role in the image’s popularity?  Again, if it does then does the popularity of the users leaving the comments play a role?

21. Questions To Be Answered  Images to most interesting  New images with a high number of views  Do they have tags or comments

22. Questions To Be Answered  Images to tags  Find out if certain tags increase popularity for a particular category  See if the number of tags create a change  Find what set of distinct repeating tags emerge from a large set of popular images  Do the same for Neutral and Unpopular images  Compare to see if the same tags exist in more than one popularity set  Will give a fairly accurate indication of weather tags are related to an image’s popularity

23. Conclusion  Gathering image data on Flickr.com  Examining multiple relationships among images and image owners to see if there are any relationships  Images/Tags  Owners/Images  Other relations