Collaborative tagging, also known as social tagging or folksonomy, allows users to apply public tags to online items, typically to make those items easier for themselves or others to find later. It has been argued that these tagging systems can provide navigational cues or "way-finders" for other users to explore information. The notion is that given that social tags are labels users create to represent topics extracted from online documents, the interpretation of these tags should allow other users to predict the contents of different documents efficiently. 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.
One significant challenge that arises in social tagging systems is the rapid increase in the number and diversity of tags. As opposed to structured annotation systems, tags provide users an unstructured, open-ended mechanism to annotate and organize web content. As users are free to create any tag to describe any resource, it leads to what is referred to as the vocabulary problem. 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. However, a number of studies have shown that structures do emerge at the semantic level – indicating that there are cohesive forces driving the emergent structures in a social tagging system.
Distinction between descriptive and predictive models
Just like any social phenomena, behavioral patterns in social tagging systems can be characterized by either a descriptive or predictive model. While descriptive models ask the question of "what", predictive models go deeper to also ask the question of "why" by attempting to provide explanations for the aggregate behavioral patterns. While there may be no general agreement on what an acceptable explanation should be like, many believe that a good explanation should have a certain level of predictive accuracy.
Descriptive models typically are not concerned with explaining the actions of individuals. Instead, they focus on describing the patterns that emerge as individual behavior is aggregated in a large social information system. Predictive models, however, attempt to explain aggregate patterns by analyzing how individuals interact and link to each other in ways that bring about similar or different emergent patterns of social behavior. In particular, a mechanism-based predictive model assumes a certain set of rules governing how individuals interact with each other, and understand how these interactions could produce aggregate patterns as observed and characterized by descriptive models. Predictive models can therefore provide explanations to why different system characteristics may lead to different aggregate patterns, and can therefore potentially provide information on how systems should be designed to achieve different social purposes.
Descriptive models
Information theory models
For most tagging systems, the total number of objects being tagged far exceeds the total number of tags in the collective vocabulary. If a single tag in this system is specified, many documents would match, so that using single tags cannot effectively isolate any one document. However, some documents are more popular or important than others, which is reflected in the number of bookmarks per document. Thus, the focus should be on how well the mapping of tags to documents retains information about the distribution of the documents. Information theory provides a framework to understand the amount of shared information between two random variables. The conditional entropy measures the amount of entropy remaining in one random variable when the value of a second random variable is known.