On the Navigability of Social Tagging Systems

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

Example: Library of Congress
The Library of Congress Classification System:
21 basic classes, detailed subclasses
In one year (2001-02), the LoC cataloged 310,235 bibliographic volumes at an avg. cost of $94.58 per volume
each area is developed by an expert according to demands of cataloging
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 .

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
A folksonomy is a tuple F := ( U, T, R, Y ) where (cf. [Hotho 2006])
- the three disjoint, finite sets U, T, R correspond to
a set of persons or users u  U
a set of tags t  T and
a set of resources or objects r  R
- Y  U × T × R , called set of tag assignments

Taxonomies vs. Folksonomies
A folksonomy is a user-generated classification, emerging through bottom-up consensus
captures language of users
yields a “democratic” and emergent classification
no explicitly defined relationship between terms, a flat namespace
co-evolves with changes in the environment
facilitates navigation
[1] http://www.iskoi.org/doc/folksonomies.htm

Factors influencing navigability
Motivations for Tagging
Future Retrieval
Contribution and Sharing
Attracting Attention (Flickr)
Play and Competition (ESP Game)
Self Presentation
Opinion Expression
Task Organization (“ toread ”)
Social Signalling (“ for:scott ”)
Money (Amazon Mechanical Turk)
Categorization / Description
Gupta et al. 2010
Kinds of Tags
Content-based
Context-based
Attribute Tags
Ownership Tags
Subjective Tags
Organizational Tags
Purpose Tags
Factual Tags
Personal Tags
Self-referential tags
Tag Bundles

Navigation with Tags
1. Tag clouds & tag selection strategies
2. Tag hierarchy generation
3. Tag Cloud Display Formats
4. Tag Evolution
Gupta et al. 2010

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.

Tag Clouds are Supposed to be Efficient Tools for Navigating Tagging Systems
The Navigability Assumption :
An implicit assumption among designers of social tagging systems that tag clouds are specifically useful to support navigation.
This has hardly been tested or critically reflected in the past.
Navigating tagging systems via tag clouds:
1) The system presents a tag cloud to the user.
2) The user selects a tag from the tag cloud.
3) The system presents a list of resources tagged with the selected tag.
4) The user selects a resource from the list of resources.
5) The system transfers the user to the selected resource, and the process potentially starts anew.

Defining Navigability
A network is navigable iff:
There is a path between all or almost all pairs of nodes in the network.
Formally:
There exists a giant component
The effective diameter is low (bounded by log n)
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)

Navigability: Examples Example 1: Not navigable : No giant component Example 2: Not navigable : giant component, BUT avg. shortest path > log 2 (9)

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 ?

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)

Bi-partite Nature of Tag Clouds

Tag Cloud Networks

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

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

User Interface constraints Tag Cloud Size n topN resources (topN most common algorithm) Pagination of resources / tag k resources shown / page (reverse chronological ordering)

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

Findings
For certain specific, but popular, tag cloud scenarios, the so-called Navigability Assumption does not hold.
While we could confirm that tag-resource networks have efficient navigational properties in theory, we found that popular user interface decisions significantly impair navigability.
These results make a theoretical and an empirical argument against existing approaches to tag cloud construction. New approaches are needed.

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)

Recovering Navigability in Social Tagging Systems Instead of reverse-chronological ordering of resources, we apply a random ordering.

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.

Trade-off between Semantic and Navigational Properties
Semantic Penalty:
Random ordering of links is counter-intuitive for navigation
There is a semantic penality imposed by maximizing for navigability
Navigational Penalty:
Semantic ordering of links might impair overall navigability
There is a navigational penality imposed by maximizing for semantic relatedness
Trade-Off Hypotheses

Outlook: Evaluating Folksonomies Lerman et al 2010

Conclusions
Social computation and social media:
Certain properties of social media (such as navigability) are emergent properties, they are beyond the direct influence of system designers
User interface constraints can effect (but do not determine) these emergent properties
By studying the relationship between user interface and network phenomena, system engineers can influence (some aspects of) emergent system properties

Ongoing and future work
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.
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.
(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.

End of Presentation
Thank you!
Markus Strohmaier
[email_address]
Graz University of Technology, Austria

On the Navigability of Social Tagging Systems

by Markus Strohmaier

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