This paper focuses on users and tags from the user point of view, not from the object-tag point of view.
The paper refers to the nine tag classes (feels kinda contrary to talk about classes for tags...) presented by Golder et al. (2006), and present three classes system with
- factual tags (Golder: item topics, kinds of item, category refinements)
- subjective tags (Golder: item qualities)
- personal tags (Golder: item ownership, self-reference, tasks organisation)
The authors conducted their research on MovieLens system where they had introduced a tagging system that was tested over a period of a month. The distribution over the tags was
- 63% factual
- 29% subjective
- 3% personal
- 5% other
- names were related to the broad subject area such as biology or mathematics (factual);
- the name additionally had indication about the intended audience (factual);
- the name only had indication of the intended audience (factual);
- the name indicated a sub-area such as “trigonometria,” a precise theme such as “brain” or a pluridiciplinary subject such as “watter” (factual);
- also names that identified other things were used such as “easy” (subjective),
- many indicated names of people and other acronymes whose meaning was not identifiable for an “outsider” (personal).
- names were mostly marked in the language of the user.
Some worth mentioning finding and something to look for or compare with in learning resources experiments
About half of the tags were tags that the user had previously applied, thus they conclude that clearly habit and investment influence tagging behaviour and grows stronger as users apply more tags (I'm not sure whether they mean that as more tags (bigger variety of tags) are used by the tagger or as more items are tagged in general (with using a small variety of tags..). However they state that habit and investment aren't the only factors that contribute to vocabulary evolution.
On the act on tagging
Users who view tags by other people before tagging their first tag are more likely to have their tags influenced by other taggers community. So community affects user's personal vocabulary and it is stronger on user's first tag if they have been exposed for others' tags. This is to consider when designing a tool!
On the convergence of vocabularies
Seems like (quite self evidently) that if people see other's (e.g. they are proposed, are autofilled when typing, ...) tags while they are tagging vocabularies are more likely to converge than if users are working on their own. This is important to think of when we are designing our tool: are we going to show only user's own tags, all other users' tags, only most used tags or make a ready-made set of desirable tags on a given resource. Plus, how will the thesaurus term affect on the tagging culture. This could actually provide an interesting research possibility: to have different tagging interfaces for users and see how tags would differ!
On tags and how they are useful for different user's tasks
Different classes of tags approve useful for different tasks that the user has. The “taskonomy” is: self-expression (helps to express opinion), organising, learning about the given movie, finding, decision support.
It seems like personal tags are found useful only for self-organising (which hints to the direction that I think of their usefulness for PKM), whereas factual tags are good for learning about the movie and to help finding it. Subjective tags are found overwhelmingly good for self-expression, but also to support decision making process (!) (although only 1/3 of people who did not tag thought so).
All in all, all tags were mostly found useful in self-expressing and organising. Note, 23% of people who did not tag found them also helpful for organising! What is interesting and worth noting for our development is that people did not like seeing other people's personal tags, to which the authors mention that maybe a design decision needs to be taken on whether to have some way to choose to keep tags only private to the tagger, i.e. how to strike balance between other benefits of tagging and the privacy that people might need.
Also, another design question is related to people who did not tag, there were overwhelming more non-taggers in the group that had not seen examples of tags than in the one that had seen them in their tagging interface. To remember, though, pre-existing tags affect future tagging behaviour.
Moreover, the authors suggest that it could be useful to try to foresee some way to classify tags in those above mentioned classes, both automated ways to infer tags and interface designs should be considered.
Good references to check:
 C. Cattuto, V. Loreto, and L. Pietronero. Semiotic dynamics in online social communities. In The European Physical Journal C (accepted). Springer-Verlag, 2006.
 R. B. Cialdini. Influence Science and Practice. Allyn and Bacon, MA, USA, 2001.
 D. Cosley, S. K. Lam, I. Albert, J. Konstan, and J. Riedl. Is seeing believing? How recommender system interfaces affect users’ opinions. In CHI, 2003.
 S. Golder and B. A. Huberman. The structure of collaborative tagging systems. Journal of Information Science (accepted), 2006.
 M. Guy and E. Tonkin. Tidying up Tags? D-Lib Magazine, 12(1):1082–9873, 2006.
 T. Hammond, T. Hannay, B. Lund, and J. Scott. Social bookmarking tools : A general review. D-Lib Magazine, 11(4), April 2005.