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Models of collaborative tagging
Many have argued that social tagging or collaborative tagging systems can provide navigational cues or “way-finders” 〔Kang, R., Fu, W.-T., & Kannampallil, T. (2010). Exploiting Knowledge-in-the-head and Knowledge-in-the-social-web: Effects of Domain Expertise on Exploratory Search in Individual and Social Search Environments. In Proceedings of the ACM Conference on Computer-Human Interaction, Atlanta, GA.〕〔Furnas, G. W., Fake, C., Von Ahn, L., Schachter, J., Golder, S., Fox, K., Davis, M., Marlow, C., and Naaman, M. Why Do Tagging Systems Work? in CHI '06 Extended Abstracts on Human Factors in Computing Systems. (2006). Montréal, Québec, Canada.〕 for other users to explore information. The notion is that, given that social tags are labels that users create to represent topics extracted from Web documents, interpretation of these tags should allow other users to predict 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 the 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.〔G. W. Furnas, T. K. Landauer, L. M. Gomez, and S. T. Dumais, "The vocabulary problem in human-system communication," Communications of the ACM, vol. 30, no. 11, pp. 964-971, 1987.〕 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 a 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 inherent "unstructuredness" of social tags may hinder their potential as navigational cues for searchers because the diversities of users and 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 that are driving the emergent structures in a social tagging system.
== The 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 to the aggregate behavioral patterns 〔Hedstrom, Peter (2005). Dissecting the social. On the principle of analytic sociology. Cambridge, UK.〕 While there may be no general agreement on what an acceptable explanation should be like, many believe that a good explanation should have certain level of predictive accuracy. Descriptive models of social tagging typically are not concerned with explaining the actions of single individuals but 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 rule that 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.
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