際際滷

際際滷Share a Scribd company logo

Panel: Social Tagging and Folksonomies: Indexing, Retrieving... and Beyond? - Searching and browsing via tag clouds

Download as PPT, PDF
J
jacekg

Panel presentation from ASIST'2011 panel: Social Tagging and Folksonomies: Indexing, Retrievingand Beyond? Jacek Gwizdka's presentation on cognitive load during search and browsing via tag clouds. And on he role of tags in information search and navigation between documents.

1 of 31
Download to read offline
Panel: Social Tagging and Folksonomies: Indexing, Retrievingand Beyond? Searching and browsing via tag clouds Jacek Gwizdka Department of Library and Information Science Rutgers University Sunday, Oct 09, 2011 CONTACT: www.jsg.tel
Process of Tagging Users associate tags with web resources Tags serve in social, structural, and semantic role structural role: starting points for navigation; helping users to orient themselves semantic role: description of a set of associated resources
Tag Clouds
My Claims Tag Clouds help in information search by saving searchers effort Tag Clouds do not support browsing tasks do not show relationships and do not show history Not just claims
Research Question Do tag clouds benefit users in search tasks?
User Interface with Overview Tag Cloud Our retrieval system populated with data from delicious List UI Overview Tag Cloud UI Search Result List Tag Cloud
User Actions in Two Interfaces 1. List 2. Overview Tag Cloud click
Experiment Design 37 participants Working memory assessed using memory span task (Francis & Neath 2003) Within subject design with 2 factors: task and user interface Tasks everyday information search (e.g., travel, shopping) at two levels of task complexity Four task rotations for each of two user interfaces
Measures Task completion time Cognitive effort: from mouse clicks: user decisions expressed as user selection of search terms = number of queries , opening documents to view from eye-tracking reading effort measures: (based on intermediate reading model ) scanning vs. reading; length of reading sequences; reading fixation duration, number of regression fixations in reading sequence, spacing of fixations in reading sequence. Task outcome = relevance * completeness
Results
Results : Time and User Behavior Overview Tag Cloud + List made users faster and more efficient less time on task: 191s in Overview+List vs. 261s in List UI less queries: 7 in Overview+List vs. 8.3 in List UI no significant differences in task outcomes Overview Tag Cloud facilitated formulation of more effective queries
Results : Cognitive Effort Overview Tag Cloud + List required less effort, higher efficiency less fixations (total and mean reading seq len) more efficient less regressions less difficulty in reading List Overview Tag Cloud + List
Results : Cognitive Effort Overview Tag Cloud + List required less effort, higher efficiency less fixations (total and mean reading seq len) more efficient less regressions less difficulty in reading Comparing only results list region in two UI conditions less effort invested in results list in Overview Tag Cloud + List Overview Tag Cloud helped to lower cognitive demands List Overview Tag Cloud + List
Did Tag Cloud Help All Users? No there are individual differences Two users, same UI and same task
Is Tag Cloud Helpful? Yes! Overview Tag Cloud + List UI made people faster and required less effort also reflected in a number of eye-tracking measures
Browsing large sets of tagged documents
An Example of Browsing (CiteULike) A typical model of browsing with tag clouds: Pivot browsing : a lightweight navigation mechanism 1. information 2. retrieval 3. algorithms 4. phylogeny
Is There a Problem?
Users Conceptualizations The labyrinth being lost The journey switching direction and being stack The space increasing distance, and continuity 18 participants
Whats the Problem? Users feel lost experience switching, yet expect some continuity In Pivot Browsing each step is treated as a separate move View is re-oriented - New list of documents along with their tags At each step context is switched Relationships between steps are not shown e.g., overlap between tag clouds not indicated Pivot browsing seems to be not lightweight conceptualizing multiple tags assigned in different quantities to different documents is difficult
Research Questions How can we support continuity in tag-space browsing? How can we promote better understanding of tag-document relationships (sensemaking) ?
Recall : Example of Navigation (CiteULike) 1. information 2. retrieval 3. algorithms 4. phylogeny
User Interface with History tag clouds (Tag Trails) Supporting continuity in tag-space navigation by providing history information retrieval algorithms phylogeny History tag clouds
User Interface with Heat map (Tag Trails 2) Supporting continuity in tag-space navigation by providing history and making (some) relationships (more) explicit Tag cloud Results list Column-tags: most recently visited tags from left to right Row-tags: selection of most frequent tags Cells color-coded according to tags df Heat map
Summary & Conclusions Tagging metadata for free: does the effort pay off? Yes, but not for all tasks Tag clouds helpful in search tasks but to support browsing new presentations of tags needed
Thank you! Questions? Jacek Gwizdka | contact: http://jsg.tel Related publications: Gwizdka, J. (2009a). What a difference a tag cloud makes: Effects of tasks and cognitive abilities on search results interface use. Information Research, 14(4), paper 414. Available online at <http://informationr.net/ir/14-4/paper414.html> Gwizdka, J. (2010c). Of kings, traffic signs and flowers: Exploring navigation of tagged documents. In Proceedings of Hypertext2010 (pp. 167-172). ACM Press. Gwizdka, J. & Bakelaar, P. (2009a). Tag trails: Navigating with context and history. CHI 09 extended abstracts (pp. 4579-4584). ACM Press. Gwizdka, J. & Bakelaar, P. (2009b). Navigating one million tags. Short paper and poster presented at ASIS&T2009, Vancouver, BC, Canada. Cole, M.J. & Gwizdka, J. (2008). Tagging semantics: Investigations with WordNet. Proceedings of JCDL2008. ACM Press. Gwizdka, J. & Cole, M.J. (2007). Finding it on Google, finding it on del.icio.us. In L. Kov叩cs, N. Fuhr, & C. Meghini (Eds.), Lecture notes in computer science (LNCS): Vol. 4765. Research and advanced technology for digital libraries, ECDL2007. (pp. 559-562). Springer-Verlag
Extra 際際滷s Intro to Reading model Tag cloud examples
Introducing Reading Model Scanning fixations provide some semantic information limited to foveal visual field (1属 visual acuity) (Rayner & Fischer, 1996) Reading fixation sequences provide more information than isolated scanning fixations information is gained from the larger parafoveal region (5属 beyond foveal focus; asymmetrical, in dir of reading) (Rayner et al., 2003) some types of semantic information is available only through reading sequences We implemented the E-Z Reader reading model (Reichle et al., 2006) Lexical fixations duration >113 ms (Reingold & Rayner, 2006) Each lexical fixation is classified to Scanning or Reading (S,R) These sequences used to create a two-state model
Reading Model States and Characteristics Two states: transition probabilities Number of lexical fixations and duration
Example Reading Sequence
Tag Clouds Everywhere!

More Related Content

Panel: Social Tagging and Folksonomies: Indexing, Retrieving... and Beyond? - Searching and browsing via tag clouds

  • 1. Panel: Social Tagging and Folksonomies: Indexing, Retrievingand Beyond? Searching and browsing via tag clouds Jacek Gwizdka Department of Library and Information Science Rutgers University Sunday, Oct 09, 2011 CONTACT: www.jsg.tel
  • 2. Process of Tagging Users associate tags with web resources Tags serve in social, structural, and semantic role structural role: starting points for navigation; helping users to orient themselves semantic role: description of a set of associated resources
  • 4. My Claims Tag Clouds help in information search by saving searchers effort Tag Clouds do not support browsing tasks do not show relationships and do not show history Not just claims
  • 5. Research Question Do tag clouds benefit users in search tasks?
  • 6. User Interface with Overview Tag Cloud Our retrieval system populated with data from delicious List UI Overview Tag Cloud UI Search Result List Tag Cloud
  • 7. User Actions in Two Interfaces 1. List 2. Overview Tag Cloud click
  • 8. Experiment Design 37 participants Working memory assessed using memory span task (Francis & Neath 2003) Within subject design with 2 factors: task and user interface Tasks everyday information search (e.g., travel, shopping) at two levels of task complexity Four task rotations for each of two user interfaces
  • 9. Measures Task completion time Cognitive effort: from mouse clicks: user decisions expressed as user selection of search terms = number of queries , opening documents to view from eye-tracking reading effort measures: (based on intermediate reading model ) scanning vs. reading; length of reading sequences; reading fixation duration, number of regression fixations in reading sequence, spacing of fixations in reading sequence. Task outcome = relevance * completeness
  • 11. Results : Time and User Behavior Overview Tag Cloud + List made users faster and more efficient less time on task: 191s in Overview+List vs. 261s in List UI less queries: 7 in Overview+List vs. 8.3 in List UI no significant differences in task outcomes Overview Tag Cloud facilitated formulation of more effective queries
  • 12. Results : Cognitive Effort Overview Tag Cloud + List required less effort, higher efficiency less fixations (total and mean reading seq len) more efficient less regressions less difficulty in reading List Overview Tag Cloud + List
  • 13. Results : Cognitive Effort Overview Tag Cloud + List required less effort, higher efficiency less fixations (total and mean reading seq len) more efficient less regressions less difficulty in reading Comparing only results list region in two UI conditions less effort invested in results list in Overview Tag Cloud + List Overview Tag Cloud helped to lower cognitive demands List Overview Tag Cloud + List
  • 14. Did Tag Cloud Help All Users? No there are individual differences Two users, same UI and same task
  • 15. Is Tag Cloud Helpful? Yes! Overview Tag Cloud + List UI made people faster and required less effort also reflected in a number of eye-tracking measures
  • 16. Browsing large sets of tagged documents
  • 17. An Example of Browsing (CiteULike) A typical model of browsing with tag clouds: Pivot browsing : a lightweight navigation mechanism 1. information 2. retrieval 3. algorithms 4. phylogeny
  • 18. Is There a Problem?
  • 19. Users Conceptualizations The labyrinth being lost The journey switching direction and being stack The space increasing distance, and continuity 18 participants
  • 20. Whats the Problem? Users feel lost experience switching, yet expect some continuity In Pivot Browsing each step is treated as a separate move View is re-oriented - New list of documents along with their tags At each step context is switched Relationships between steps are not shown e.g., overlap between tag clouds not indicated Pivot browsing seems to be not lightweight conceptualizing multiple tags assigned in different quantities to different documents is difficult
  • 21. Research Questions How can we support continuity in tag-space browsing? How can we promote better understanding of tag-document relationships (sensemaking) ?
  • 22. Recall : Example of Navigation (CiteULike) 1. information 2. retrieval 3. algorithms 4. phylogeny
  • 23. User Interface with History tag clouds (Tag Trails) Supporting continuity in tag-space navigation by providing history information retrieval algorithms phylogeny History tag clouds
  • 24. User Interface with Heat map (Tag Trails 2) Supporting continuity in tag-space navigation by providing history and making (some) relationships (more) explicit Tag cloud Results list Column-tags: most recently visited tags from left to right Row-tags: selection of most frequent tags Cells color-coded according to tags df Heat map
  • 25. Summary & Conclusions Tagging metadata for free: does the effort pay off? Yes, but not for all tasks Tag clouds helpful in search tasks but to support browsing new presentations of tags needed
  • 26. Thank you! Questions? Jacek Gwizdka | contact: http://jsg.tel Related publications: Gwizdka, J. (2009a). What a difference a tag cloud makes: Effects of tasks and cognitive abilities on search results interface use. Information Research, 14(4), paper 414. Available online at <http://informationr.net/ir/14-4/paper414.html> Gwizdka, J. (2010c). Of kings, traffic signs and flowers: Exploring navigation of tagged documents. In Proceedings of Hypertext2010 (pp. 167-172). ACM Press. Gwizdka, J. & Bakelaar, P. (2009a). Tag trails: Navigating with context and history. CHI 09 extended abstracts (pp. 4579-4584). ACM Press. Gwizdka, J. & Bakelaar, P. (2009b). Navigating one million tags. Short paper and poster presented at ASIS&T2009, Vancouver, BC, Canada. Cole, M.J. & Gwizdka, J. (2008). Tagging semantics: Investigations with WordNet. Proceedings of JCDL2008. ACM Press. Gwizdka, J. & Cole, M.J. (2007). Finding it on Google, finding it on del.icio.us. In L. Kov叩cs, N. Fuhr, & C. Meghini (Eds.), Lecture notes in computer science (LNCS): Vol. 4765. Research and advanced technology for digital libraries, ECDL2007. (pp. 559-562). Springer-Verlag
  • 27. Extra 際際滷s Intro to Reading model Tag cloud examples
  • 28. Introducing Reading Model Scanning fixations provide some semantic information limited to foveal visual field (1属 visual acuity) (Rayner & Fischer, 1996) Reading fixation sequences provide more information than isolated scanning fixations information is gained from the larger parafoveal region (5属 beyond foveal focus; asymmetrical, in dir of reading) (Rayner et al., 2003) some types of semantic information is available only through reading sequences We implemented the E-Z Reader reading model (Reichle et al., 2006) Lexical fixations duration >113 ms (Reingold & Rayner, 2006) Each lexical fixation is classified to Scanning or Reading (S,R) These sequences used to create a two-state model
  • 29. Reading Model States and Characteristics Two states: transition probabilities Number of lexical fixations and duration

Editor's Notes

  • #4: socially constructed tags are often presented in a form of a cloud .