gwizdka asist 2011 panel: social tagging and folksonomies: indexing, retrieving…and beyond?...

8/3/2019 Gwizdka ASIST 2011 Panel: Social Tagging and Folksonomies: Indexing, Retrievingand Beyond? Searching and browsing via tag clouds

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Jacek GwizdkaDepartment of Library and Information Science

Rutgers University

Sunday, Oct 09, 2011

Panel: Social Tagging and Folksonomies:

Indexing, Retrievingand Beyond?

Searching and browsing via tag clouds

CONTACT:

www.jsg.tel


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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


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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


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User Actions in Two Interfaces

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ViewSearchResults

Viewone result

page

Start

End

Delete TagNew Tag

ClickResult

URLClickBack

button Click Done &enter answer

1. List

2.Overview

Tag Cloud

click


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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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Fact finding

Information

gathering Fact finding

Information

gathering

Fact findingInformation

gathering

Information

gatheringFact finding

Information

gatheringFact finding Fact finding

Information

gathering

Information


Information



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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+ Listmade users faster and more efficient

less time on task: 191s inOverview+List vs. 261s in List UI

less queries: 7 inOverview+List vs. 8.3 in List UI

no significant differences in task outcomes

Overview Tag Cloudfacilitated formulation of more effective queries

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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

12List Overview Tag Cloud + List


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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 onlyresults list region in two UI conditions

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

Overview Tag Cloudhelped to lower cognitive demands

13List Overview Tag Cloud + List


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Did Tag Cloud Help All Users?

No there are individual differences

Two users, same UI and same task 14


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Is Tag Cloud Helpful?

Yes!

OverviewTag Cloud+ ListUI

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


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Is There a Problem?

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Users ConceptualizationsThe labyrinth

being lost

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The journey

switching

directionand being stack

The space

increasingdistance,

and continuity18 participants


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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 notlightweight

conceptualizing multiple tags assigned in different quantities to different

documents is difficult


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Research Questions

How can we support continuity in tag-space browsing?

How can we promote better understanding

of tag-document relationships (sensemaking)?


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Recall: Example of Navigation (CiteULike)

1. information 2. retrieval 3. algorithms 4. phylogeny


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User Interface with History tag clouds (Tag Trails)

Supporting continuity in tag-space navigation by providing history

information retrieval algorithms phylogeny

History

tag clouds


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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 ofmost frequent tags

Cells color-codedaccording to tags df

Heat map


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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

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. Kovcs, 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-V

erlag


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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 (1r visual acuity) (Rayner & Fischer, 1996)

Reading fixation sequences provide more information than

isolated scanning fixations

information is gained from the larger parafoveal region (5r 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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ReadingModel 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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