• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Improving Personal Tagging Consistency Through Visualization Of Tag

Improving Personal Tagging Consistency Through Visualization Of Tag






Total Views
Views on SlideShare
Embed Views



4 Embeds 14

http://trisha.snappages.com 8
http://www.slideshare.net 4
http://static.slidesharecdn.com 1
http://www.linkedin.com 1



Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

    Improving Personal Tagging Consistency Through Visualization Of Tag Improving Personal Tagging Consistency Through Visualization Of Tag Presentation Transcript

    • 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
    • Content
      Research Question
      Tag A
      Tag B
      Results & Discussion
      Tagging consistency is important for users to organize things effectively and to retrieve them efficiently later on.
    • 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
    • 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.
    • 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?
    • 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
    • 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.
    • Methodology
      2*2 experiment design
    • 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
    • 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‖
    • Methodology
      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
    • Methodology
      100 pictures selected from Flickr, tagged as “nature”, “city”, or “people”
      20 were stimuli, and other 80 were filler pictures
      40 participants, including 10 females and 30 males, aged from 20 to 31
      All are experienced tagging users
      Two tagging sessions, with a disruptive interval in between.
    • 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
    • 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)
    • Results
      Testing of Hypothesis 2
      aKruskal-Wallis-test.*Significant differences at p<.05
    • 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.
    • 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
    • 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.
    • 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.
    • Thank you for your attention.
      Q & A