This document discusses navigability in social tagging systems. It begins by defining social tagging systems and folksonomies. It then examines factors that influence navigability in social tagging systems like motivations for tagging. It analyzes how tag clouds and hierarchies can be used for navigation but notes that user interface constraints like tag cloud size and pagination can impair navigability. It concludes that certain popular approaches to tag clouds do not support navigability and new approaches are needed that consider the trade-off between semantic and navigational properties.
1. Navigability in Social Tagging Systems Markus Strohmaier Knowledge Management Institute, Graz University of Technology, Austria e-mail: markus.strohmaier@tugraz.at web: http://www.kmi.tugraz.at/staff/markus In collaboration with: Denis Helic, Christoph Trattner, Keith Andrews, Christian Körner D. Helic, C. Trattner, M. Strohmaier and K. Andrews, On the Navigability of Social Tagging Systems, The 2 nd IEEE International Conference on Social Computing (SocialCom 2010), Minneapolis, Minnesota, USA
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7. Social Navigation of Tagging Systems … refers to systems in which a user’s navigation is guided by the behavior of others [Dieberger 1997]. In such systems, the link structure is not created by a single person, but it is the result of aggregating information from a group of users . In this sense, navigability of tagging systems is mostly beyond the direct control of system designers . A. Dieberger, “Supporting social navigation on the world wide web,” Int. J. Hum.-Comput. Stud., vol. 46, no. 6, pp. 805–825, 1997.
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10. Navigability: Examples Example 1: Not navigable : No giant component Example 2: Not navigable : giant component, BUT avg. shortest path > log 2 (9)
11. Navigability: Examples Example 3: Navigable : Giant component AND avg. shortest path ≤ 2 < log 2 (9) Is this efficiently navigable? There are short paths between all nodes, but can an agent or algorithm find them with local knowledge only ?
12. Efficiently navigable A network is efficiently navigable iff: If there is an algorithm that can find a short path with only local knowledge (with branching factor k), and the delivery time of the algorithm is bounded polynomially by log k (n). Example 4: Efficiently navigable, if the algorithm knows it needs to go through A B C A B C J. Kleinberg. The small-world phenomenon: An algorithmic perspective. Proc. 32nd ACM Symposium on Theory of Computing, 2000. Also appears as Cornell Computer Science Technical Report 99-1776 (October 1999)
15. Navigability of Social Tagging Systems The usefulness of tag clouds for navigation is sensitive to the phase of adoption of the social tagging system established systems, many users New system, few users
16. Navigability of Social Tagging Systems . Tagging networks are navigable power-law networks. For power law networks, efficient sub-linear decentralised navigation algorithms exist. „ Hub“ tags
17. User Interface constraints Tag Cloud Size n topN resources (topN most common algorithm) Pagination of resources / tag k resources shown / page (reverse chronological ordering)
18. How UI constraints effect Navigability . Limiting the tag cloud size n to practically feasible sizes (e.g. 5, 10, or more) does not influence navigability (this is not very surprising). BUT : Limiting the out-degree of high frequency tags k (e.g. through pagination with resources sorted in reverse-chronological order) leaves the network vulnerable to fragmentation. This destroys navigability of prevalent approaches to tag clouds. Pagination Tag Cloud Size
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20. Navigability: Examples Example 5: Generating navigable structures via long- range links J. Kleinberg. The small-world phenomenon: An algorithmic perspective. Proc. 32nd ACM Symposium on Theory of Computing, 2000. Also appears as Cornell Computer Science Technical Report 99-1776 (October 1999)
21. Recovering Navigability in Social Tagging Systems Instead of reverse-chronological ordering of resources, we apply a random ordering.
22. Efficient Navigability in Social Tagging Systems Instead of random ordering, we use hierarchical background knowledge for ranking paginated resources [Kleinberg 2001]. J. M. Kleinberg, “Small-world phenomena and the dynamics of information,” in Advances in Neural Information Processing Systems (NIPS), 14. MIT Press, 2001, p. 2001.