The first paper in this session is Of Categorizers and Describers: An Evaluation of Quantitative Measures for Tagging Motivation by Christian Körner. This talk looks into the motivation for tagging. A categorizer does not reuse his/her own tags often, so the tags do not contain much synonyms, whereas a describer contains lots of synonym tags. For approximating tagging motivation, they measure the tag/resource ratio (how many tags does a user use), orphaned tag ratio, overlap factor, and tag/title intersection ratio (how likely does a user choose words from the title as tags). They also look into the properties of the measures. For their quantitative evaluation, users who prefer folksonomy-based recommendation (describers) can be best identified by a high tag/title intersection ratio, and users who prefer personomy-based recommendation (categorizers) can be best identified by a log tag/resource ratio.
The second paper is Of Kings, Traffic Signs and Flowers: Exploring Navigation of Tagged Documents by Jacek Gwizdka. Can we improve the navigation process of tagged documents like CiteULike for example? One way is to provide history to support continuity in tag-space navigation using tag trails (eg. visualize with tag clouds). A user interface can be created with a heat map of history and relationship between set of documents that were visited. Jacek uses the concept of kingdom to describe the hierarchical relationship between tags. From the experiment, subjects experienced switching. Users desire to have a simpler user interface for tagging navigation continuity.
The third paper is The impact of resource title on tags in collaborative tagging systems by Marek Lipczak and Evangelos Milios. The authors wanted to figure out if title words are important for finding a profile tag and they found out from Delicious and CiteULike that title words are important. They looked into synonymous tags and they were interested into the frequencies of one form and another form. There is a relation between title and tags but it introduces redundancy and variability.
The fourth and last paper is Conversation Tagging in Twitter by Jeff Huang. In Jeff's talk, they looked into reviewing trends for 100s of newly coined popular tags from Twitter and Delicious and did a statistical analysis of time-series for popular tags from Twitter and Delicious. They claim that this is the first large-scale dataset of Twitter hashtags. This is good for characterizing tag trends. They found that many tags in Twitter are conversational, tags in Delicious are purely organizational.
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