Tagging Systems and Their Effect on Resource Popularity Austin Wester

Background & Related Work

 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

 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

Methodology

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 images/day or 2.1 million in a month

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

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

Analyzing The Data

 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.

Analyzing The Data  Use Pajek, VS.net 2005

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

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

Analyzing The Data  Convert bipartite graphs into 1-mode

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%

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

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.

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

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

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?

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

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

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