It has been argued that these tagging systems can provide navigational cues or "way-finders" for other users to explore information.
Social tags are arguably more important in exploratory search, in which the users may engage in iterative cycles of goal refinement and exploration of new information (as opposed to simple fact-retrievals), and interpretation of information contents by others will provide useful cues for people to discover topics that are relevant.
As users are free to create any tag to describe any resource, it leads to what is referred to as the vocabulary problem.
[3] Because users may use different words to describe the same document or extract different topics from the same document based on their own background knowledge, the lack of any top-down mediation may lead to an increase in the use of incoherent tags to represent the information resources in the system.
In other words, the lack of structure inherent in social tags may hinder their potential as navigational cues for searchers because the diversities of users and their motivation may lead to diminishing tag-topic relations as the system grows.
Instead, they focus on describing the patterns that emerge as individual behavior is aggregated in a large social information system.
A 2008 paper by Ed Chi and Todd Mytkowicz showed that the entropy of documents conditional on tags, H(D|T), is increasing rapidly.
[6] This suggests that, even after knowing completely the value of a tag, the entropy of the set of documents is increasing over time.
While these observations provided evidence against the proposed vocabulary problem, they also initiated research investigating how and why tag proportions tended to converge over time.
One explanation for the stability was that there was an inherent propensity for users to "imitate" word use of others as they create tags.
[8] Specifically, the convergence of tag choices was simulated by a process in which a colored ball was randomly selected from an urn, then replaced in the urn along with an additional ball of the same color, simulating the probabilistic nature of tag reuse.
The memory-based Yule-Simon (MBYS) model[7] attempts to explain tag choices by a stochastic process.
[12] Assuming that people in the same culture tend to have shared structures – such as using similar vocabularies and their corresponding meanings to conform and communicate, users of the same social tagging system may also share similar semantic representations of words and concepts, even when the use of tags may vary across individuals at the word level.
As such, part of the reason for the stability of social tagging systems can be attributed to the shared semantic representations among the users, such that users may have relatively stable and coherent interpretation of information contents and tags as they interact with the system.
[13][14] The model also predicts that the folksonomies in the system reflect the shared semantic representations of the users.
This is in sharp contrast to conclusions derived based on a purely information-theoretical approach, which assumes that humans search and evaluate information at the word level.