Improving Personal Tagging Consistency Through Visualization Of Tag

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  • 1. HCI International 2009
    19-24 July 09, San Diego, CA, USA
    Improving Personal Tagging Consistency through Visualization of Tag Relevancy
    Dr. Qin Gao*, Yusen Dai, and Kai Fu
    Institute of Human Factors & Ergonomics
    Dept. of Industrial Engineering, Tsinghua University
  • 2. Content
    Introduction
    Research Question
    Tag A
    Tag B
    Methodology
    Results & Discussion
    Conclusion
    Tagging consistency is important for users to organize things effectively and to retrieve them efficiently later on.
  • 3. Introduction
    Tagging has emerged as a new means of information organization and retrieval
    Tagging is easy to use, flexible, able to harvest the intelligence of the crowd
    But there are many inconsistencies in tagging systems!
    Tripartite model of tagging system,
    from Halpin, Robu, & Sherpherd, 2007
  • 4. Introduction
    Vocabulary problems
    “Bad” tags: misspelt tags, badely encoded tags, mixed use of singulars and plurals, and etc.
    Inevitable semantic inconsistency: polysemy, synonym, and basic level variations. (Golder & Huberman 2006)
    Consistency between taggers
    The extent to which different users agree on selection for certain tags for specific content.
    Allowing a true representation of knowledge and multiple interpretations of the same content.
    Trends towards stabilization (Golder & Huberman, 2005)
    Consistency within individual taggers
    The extent to which individual users agree on selection for certain tags for specific content at different point in time.
  • 5. Introduction
    Consistency within individual taggers is important to individual users and to the system.
    Affecting efficiency of information organization and retrieval tasks for individual users
    Organizing information is one of the most motivation for tagging (Ames and Naaman, 2007; Marlow, et al., 2006).
    Indexing research shows that reliance on consistently used indexing cues is desired for effective access of information
    Impacts on users’ perceived usefulness of the system and their satisfaction.
    How to improve individual tagging consistency?
    Providing tag suggestions based on existing tagging pattern can shape users’ tagging behavior (Sen et al, 2006; Binkowski, 2006)
    How to present such suggestions?
    How to select tags for suggestion?
  • 6. Visualization of Tags
    The first generation
    of tag clouds
    The second generation
    of tag clouds
    Tag popularity is represented by visual cues
    Semantic relations among tags is revealed by visualization
    Semantically clustering of tags
    by Montero & Solana (2006)
    Tag clouds from Amazon, from Bateman 2007
    Nielson, 2007
  • 7. Research Question
    Goal of the study: to examine the effect of tag frequency visualization and semantically clustering on users’ tagging consistency
    Hypothesis 1: visualization of occurrence frequency of tags improves personal tag consistency and reduces users’ workload.
    Hypothesis 2: visualization of inter-tag relevancy improves personal tag consistency.
  • 8. Methodology
    2*2 experiment design
  • 9. Methodology
    Frequency visualization by font size
    the font size was determined by the following logarithm function
    Definition of font size levels
    Currenti is the font size level of the current tag
    Oiis the use frequency of the current tag
    The relationship between font size level and tag frequency
  • 10. Methodology
    ti=(d1i, d2i, d3i, …, dni)
    Visualization of tag relevancy – Semantically clustering
    Clusters of relevant tags were calculated based on co-occurrence similarity with K-means algorithm developed by Montero and Solana (2006).
    The approach was proved to reduce semantically density of tag clouds significantly.
    Definition of the vector space:
    ti=(d1i, d2i, … dni)
    cosine (t1, t2)=(t1·t2)/‖t1‖*‖t2‖
  • 11. Methodology
    Let
    and
    in two sessions
    in two sessions
    Dependent variables
    Tagging consistency
    Let Ai and Bi denote the sets of tags that assigned to the same document in two sessions, then tagging consistency with this document:
    The overall tagging consistency:
    Workload measured by NASA-TLX
    Ai and Bi denote the sets of tags that assigned to the same document in two different tagging sessions
  • 12. Methodology
    Stimuli
    100 pictures selected from Flickr, tagged as “nature”, “city”, or “people”
    20 were stimuli, and other 80 were filler pictures
    Participants
    40 participants, including 10 females and 30 males, aged from 20 to 31
    All are experienced tagging users
    Procedure
    Two tagging sessions, with a disruptive interval in between.
  • 13. Results
    aKruskal-Wallis-test.*Significant differences at p<.05
    aKruskal-Wallis-test.*Significant differences at p<.05
    aKruskal-Wallis-test.*Significant differences at p<.05
    aKruskal-Wallis-test.*Significant differences at p<.05
    Testing of hypothesis 1
  • 14. Results
    Frequency visualization has no significant impact on tagging consistency.
    Frequency visualization reduces perceived physical demand significantly, but also increases mental demand.
    An interaction effect on physical demand (χ2 = 6.4, p = .01)
  • 15. Results
    Testing of Hypothesis 2
    aKruskal-Wallis-test.*Significant differences at p<.05
  • 16. Results
    Semantically clustering improves personal tagging significantly. H2 was supported.
    But no significant difference in workload or the number of tags given by participants.
    The consistency level of participants tagging with semantically clustering is 12% higher than that of participants tagging without such visualization.
  • 17. Discussion
    Two types of tags
    General categorical tags, influenced by the basic level
    High recall but low accuracy
    Users have a strong bias to use them as first tags (Golder & Huberman, 2005).
    Relatively more consistent.
    Descriptive/specific tags, ego-centered
    High accuracy but low recall
    All participants expressed their intention to tag consistently, but often failed to do so due to limited memory.
    Major source of inconsistencies
  • 18. Discussion
    Semantically clustering of tags helps users’ tag formulation tasks and improves their consistency in identifying and deciding on specific tags
    It improves the performance of specific search and increase the attention towards tags in small fonts compared to other layouts (Schrammel et al., 2009).
    Frequency visualization does not provide support for search of specific tags.
    When used in combination with semantically clustering, it help reduce perceived physical demand.
  • 19. Conclusion
    Visualizing the relevancy among tags has a significant positive effect on tagging consistency, whereas visualizing tagging frequency does not.
    Empirical support for the effort of visualizing semantic relationships among tags
    When the tag relevancy is visualized, highlight frequently used tags can reduce perceived physical demands; however, it increases perceived mental demands as well.
    Implications for professional indexer aid design.
  • 20. Thank you for your attention.
    Contact:
    gaoqin@tsinghua.edu.cn
    http://trisha.snappages.com
    Q & A