On the Navigability of Social Tagging Systems


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We investigate if and to what extent tag clouds - a popular mechanism for interacting with social media - are useful for navigation.

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  • we will show that the Navigability Assumption only holds in very specific settings, and for the most common scenarios, we can assert that it is wrong.
  • On the Navigability of Social Tagging Systems

    1. 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
    2. 2. Example: Library of Congress <ul><li>The Library of Congress Classification System: </li></ul><ul><li>21 basic classes, detailed subclasses </li></ul><ul><li>In one year (2001-02), the LoC cataloged 310,235 bibliographic volumes at an avg. cost of $94.58 per volume </li></ul><ul><li>each area is developed by an expert according to demands of cataloging </li></ul>Issues : cost of classification, rigidity of schema, training of librarians, consistency of categories, number of categories, low adoption (vs. Dewey C.), … Goal: to arrange items so that books / articles on a given subject are found close to similar ones. to support easy navigation .
    3. 3. What are Social Tagging Systems? An Example User Resources Tags Def.: Tagging typically describes the voluntary activity of users who are annotating resources with terms (“tags”) freely chosen from an unbounded and uncontrolled vocabulary <ul><li>A folksonomy is a tuple F := ( U, T, R, Y ) where (cf. [Hotho 2006]) </li></ul><ul><li>- the three disjoint, finite sets U, T, R correspond to </li></ul><ul><ul><li>a set of persons or users u  U </li></ul></ul><ul><ul><li>a set of tags t  T and </li></ul></ul><ul><ul><li>a set of resources or objects r  R </li></ul></ul><ul><li>- Y  U × T × R , called set of tag assignments </li></ul>
    4. 4. Taxonomies vs. Folksonomies <ul><li>A folksonomy is a user-generated classification, emerging through bottom-up consensus </li></ul><ul><li>captures language of users </li></ul><ul><li>yields a “democratic” and emergent classification </li></ul><ul><li>no explicitly defined relationship between terms, a flat namespace </li></ul><ul><li>co-evolves with changes in the environment </li></ul><ul><li>facilitates navigation </li></ul>[1] http://www.iskoi.org/doc/folksonomies.htm
    5. 5. Factors influencing navigability <ul><li>Motivations for Tagging </li></ul><ul><li>Future Retrieval </li></ul><ul><li>Contribution and Sharing </li></ul><ul><li>Attracting Attention (Flickr) </li></ul><ul><li>Play and Competition (ESP Game) </li></ul><ul><li>Self Presentation </li></ul><ul><li>Opinion Expression </li></ul><ul><li>Task Organization (“ toread ”) </li></ul><ul><li>Social Signalling (“ for:scott ”) </li></ul><ul><li>Money (Amazon Mechanical Turk) </li></ul><ul><li>Categorization / Description </li></ul>Gupta et al. 2010 <ul><li>Kinds of Tags </li></ul><ul><li>Content-based </li></ul><ul><li>Context-based </li></ul><ul><li>Attribute Tags </li></ul><ul><li>Ownership Tags </li></ul><ul><li>Subjective Tags </li></ul><ul><li>Organizational Tags </li></ul><ul><li>Purpose Tags </li></ul><ul><li>Factual Tags </li></ul><ul><li>Personal Tags </li></ul><ul><li>Self-referential tags </li></ul><ul><li>Tag Bundles </li></ul>
    6. 6. Navigation with Tags <ul><li>1. Tag clouds & tag selection strategies </li></ul><ul><li>2. Tag hierarchy generation </li></ul><ul><li>3. Tag Cloud Display Formats </li></ul><ul><li>4. Tag Evolution </li></ul>Gupta et al. 2010
    7. 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.
    8. 8. Tag Clouds are Supposed to be Efficient Tools for Navigating Tagging Systems <ul><li>The Navigability Assumption : </li></ul><ul><li>An implicit assumption among designers of social tagging systems that tag clouds are specifically useful to support navigation. </li></ul><ul><li>This has hardly been tested or critically reflected in the past. </li></ul><ul><li>Navigating tagging systems via tag clouds: </li></ul><ul><li>1) The system presents a tag cloud to the user. </li></ul><ul><li>2) The user selects a tag from the tag cloud. </li></ul><ul><li>3) The system presents a list of resources tagged with the selected tag. </li></ul><ul><li>4) The user selects a resource from the list of resources. </li></ul><ul><li>5) The system transfers the user to the selected resource, and the process potentially starts anew. </li></ul>
    9. 9. Defining Navigability <ul><li>A network is navigable iff: </li></ul><ul><li>There is a path between all or almost all pairs of nodes in the network. </li></ul><ul><li>Formally: </li></ul><ul><li>There exists a giant component </li></ul><ul><li>The effective diameter is low (bounded by log n) </li></ul>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)
    10. 10. Navigability: Examples Example 1: Not navigable : No giant component Example 2: Not navigable : giant component, BUT avg. shortest path > log 2 (9)
    11. 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. 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)
    13. 13. Bi-partite Nature of Tag Clouds
    14. 14. Tag Cloud Networks
    15. 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. 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. 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. 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
    19. 19. Findings <ul><li>For certain specific, but popular, tag cloud scenarios, the so-called Navigability Assumption does not hold. </li></ul><ul><li>While we could confirm that tag-resource networks have efficient navigational properties in theory, we found that popular user interface decisions significantly impair navigability. </li></ul><ul><li>These results make a theoretical and an empirical argument against existing approaches to tag cloud construction. New approaches are needed. </li></ul>
    20. 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. 21. Recovering Navigability in Social Tagging Systems Instead of reverse-chronological ordering of resources, we apply a random ordering.
    22. 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.
    23. 23. Trade-off between Semantic and Navigational Properties <ul><li>Semantic Penalty: </li></ul><ul><li>Random ordering of links is counter-intuitive for navigation </li></ul><ul><li>There is a semantic penality imposed by maximizing for navigability </li></ul><ul><li>Navigational Penalty: </li></ul><ul><li>Semantic ordering of links might impair overall navigability </li></ul><ul><li>There is a navigational penality imposed by maximizing for semantic relatedness </li></ul>Trade-Off Hypotheses
    24. 24. Outlook: Evaluating Folksonomies Lerman et al 2010
    25. 25. Conclusions <ul><li>Social computation and social media: </li></ul><ul><li>Certain properties of social media (such as navigability) are emergent properties, they are beyond the direct influence of system designers </li></ul><ul><li>User interface constraints can effect (but do not determine) these emergent properties </li></ul><ul><li>By studying the relationship between user interface and network phenomena, system engineers can influence (some aspects of) emergent system properties </li></ul>
    26. 26. Ongoing and future work <ul><li>Intent and motivation in Social Media M. Strohmaier, C. Koerner, R. Kern, Why do Users Tag? Detecting Users' Motivation for Tagging in Social Tagging Systems , 4th International AAAI Conference on Weblogs and Social Media (ICWSM2010), Washington, DC, USA, May 23-26, 2010. </li></ul><ul><li>Social computation and emergent structures C. Körner, D. Benz, A. Hotho, M. Strohmaier, G. Stumme, Stop Thinking, Start Tagging: Tag Semantics Arise From Collaborative Verbosity , 19th International World Wide Web Conference (WWW2010), Raleigh, NC, USA, April 26-30, ACM, 2010. </li></ul><ul><li>(Intentional) knowledge acquisition from Social Media C. Wagner, M. Strohmaier, The Wisdom in Tweetonomies: Acquiring Latent Conceptual Structures from Social Awareness Streams , Semantic Search 2010 Workshop (SemSearch2010), in conjunction with the 19th International World Wide Web Conference (WWW2010), Raleigh, NC, USA, April 26-30, ACM, 2010. </li></ul>
    27. 27. End of Presentation <ul><li>Thank you! </li></ul><ul><li>Markus Strohmaier </li></ul><ul><li>[email_address] </li></ul><ul><li>Graz University of Technology, Austria </li></ul>