J a c e k G w iz d k aDepartment of Library and Information Science

Rutgers University

S u n d a y , O c t 0 9 , 2 0 11

Panel: Social Tagging and Folksonomies: Indexing, Retrieving…and Beyond?

Searching and browsing via tag clouds

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

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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…

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Research Question

• Do tag clouds benefit users in search tasks?

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User Interface with Overview Tag Cloud

Our retrieval system populated with data from delicious 6

List UI

Overview Tag Cloud UI

Search Result List Tag Cloud

User Actions in Two Interfaces

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View Search Results

View one result

page

Start

End

Delete Tag New Tag

Click Result

URL

Click “Back” button Click “Done” &

enter answer

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

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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

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Results

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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

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Results : Cognitive Effort

•verview Tag Cloud + List required less effort, higher efficiency

– less fixations (total and mean reading seq len) – more efficient

– less regressions – less difficulty in reading

12List Overview Tag Cloud + List

Results : Cognitive Effort

•verview Tag Cloud + List required less effort, higher efficiency

– less fixations (total and mean reading seq len) – more efficient

– less regressions – less difficulty in reading

•omparing only results list region in two UI conditions

– less effort invested in results list in Overview Tag Cloud + List

verview Tag Cloud helped to lower cognitive demands

13List Overview Tag Cloud + List

Did Tag Cloud Help All Users?

No – there are individual differences

Two users, same UI and same task 14

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

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Browsing large sets of tagged documents

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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?

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Users’ ConceptualizationsThe labyrinth

… being lost

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The journey

… switching direction

and being stack

The space

… increasing distance, and continuity18 participants

What’s 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 recentlyvisited tags from left to right

Row-tags: selection of most frequent tags

Cells color-coded according to tag’s 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

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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 Hypertext’2010 (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&T’2009, Vancouver, BC, Canada.

Cole, M.J. & Gwizdka, J. (2008). Tagging semantics: Investigations with WordNet. Proceedings of JCDL’2008. ACM Press.

Gwizdka, J. & Cole, M.J. (2007). Finding it on Google, finding it on del.icio.us. In L. Kovács, N. Fuhr, & C. Meghini (Eds.), Lecture notes in computer science (LNCS): Vol. 4765. Research and advanced technology for digital libraries, ECDL’2007. (pp. 559-562). Springer-Verlag

Extra Slides

• Intro to Reading model

• Tag cloud examples

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Introducing Reading Model

• Scanning fixations provide some semantic information– limited to foveal visual field (1° visual acuity) (Rayner & Fischer, 1996)

• Reading fixation sequences provide more information than isolated “scanning” fixations

– information is gained from the larger parafoveal region (5° 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

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Reading Model – States and Characteristics

• Two states: transition probabilities

• Number of lexical fixations and duration

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Example Reading Sequence

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Tag Clouds Everywhere!

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