Journal

of

Information

Science

The

folksonomy

tag

cloud

when

is

it

useful

James

Sinclair

and

Michael

Cardew-Hall

Journal

of

Information

Science

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for

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use

or

unauthorized

distribution

The

folksonomy

tag

cloud

when

is

it

useful

James

Sinclair

and

Michael

Cardew-Hall

Department

of

Engineering

The

Australian

National

University

Canberra

ACT

Australia

Abstract

The

weighted

list

known

popularly

as

a

tag

cloud

has

appeared

on

many

popular

folksonomy-based

websites

Flickr

Delicious

Technorati

and

many

others

have

all

featured

a

tag

cloud

at

some

point

in

their

history

However

it

is

unclear

whether

the

tag

cloud

is

actually

useful

as

an

aid

to

finding

information

We

conducted

an

experiment

giving

participants

the

option

of

using

a

tag

cloud

or

a

traditional

search

interface

to

answer

various

questions

We

found

that

where

the

information-seeking

task

required

specific

information

participants

preferred

the

search

interface

Conversely

where

the

information-seeking

task

was

more

general

participants

preferred

the

tag

cloud

While

the

tag

cloud

is

not

without

value

it

is

not

sufficient

as

the

sole

means

of

navigation

for

a

folksonomy-based

dataset

Keywords

collaborative

tagging

folksonomies

tag

clouds

user

interfaces

information

retrieval

Introduction

The

popularity

of

services

such

as

Flickr

and

has

created

a

great

deal

of

interest

in

tagging

systems

and

their

potential

for

collaborative

organization

of

resources

In

fact

interest

has

been

so

great

that

articles

have

appeared

in

the

popular

press

-LSB-

-RSB-

explaining

the

concept

to

audiences

who

would

normally

have

little

interest

in

information

architecture

Yahoo!

considered

these

kinds

of

systems

important

enough

to

make

the

strategic

acquisition

of

Flickr

-LSB-

-RSB-

and

Delicious

-LSB-

-RSB-

Across

the

world

wide

web

-LRB-

WWW

-RRB-

the

use

of

tagging

systems

is

expanding

rapidly

Tagging

services

Tagging

services

allow

a

participant

to

associate

freely

determined

keywords

-LRB-

called

tags

-RRB-

with

a

particular

resource

The

resource

may

be

just

about

anything

Tagging

services

exist

to

tag

an

enormous

variety

of

things

such

as

photographs

URLs

podcasts

computer

games

music

and

videos

The

emergent

dataset

arising

from

all

the

participants

tags

and

resources

is

commonly

referred

to

as

a

folksonomy

Hammond

et

al.

-LSB-

-RSB-

provide

an

excellent

review

of

tagging

services

Correspondence

to

James

Sinclair

Department

of

Engineering

The

Australian

National

University

Canberra

ACT

Australia

Email

james.sinclair@anu.edu.au

Journal

of

Information

Science

-LRB-

-RRB-

pp.

CILIP

DOI

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and

Information

Professionals

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reserved

Not

for

commercial

use

or

unauthorized

distribution

James

Sinclair

Michael

Cardew-Hall

The

concept

of

using

freely

determined

keywords

assigned

by

non-experts

is

not

a

new

one

-LSB-

-RSB-

Many

scientific

journals

use

author-generated

keywords

to

supplement

indexing

and

the

use

of

the

keywords

meta-tag

was

an

early

feature

of

the

WWW

-LSB-

-RSB-

Using

an

uncontrolled

vocabulary

in

this

way

is

less

costly

than

employing

an

expert

to

perform

a

domain

analysis

to

determine

an

appropriate

classification

scheme

Allowing

authors

to

classify

their

own

work

also

avoids

the

ongoing

cost

of

employing

an

expert

to

classify

resources

added

to

the

repository

There

are

problems

however

with

allowing

authors

control

over

classification

Firstly

there

is

the

question

of

expertise

The

issue

is

summarized

by

Barton

et

al.

The

basic

problem

is

this

who

is

best

placed

to

add

subject-related

metadata

for

maximum

resource

discovery

the

author

who

may

know

the

subject

area

and

its

terminology

well

or

a

metadata

specialist

who

may

be

better

placed

to

step

back

and

think

about

all

the

potential

users

of

a

resource

or

about

consistency

of

subject

terms

and

classifications

across

a

repository

archive

or

network

so

that

like

really

is

classified

with

like

-LSB-

-RSB-

Secondly

there

is

the

issue

of

deliberate

misuse

of

metadata

There

is

a

perception

that

authorgenerated

metadata

lacks

credibility

Thomas

and

Griffin

-LSB-

-RSB-

argue

that

there

is

little

incentive

for

authors

to

put

time

and

effort

into

providing

quality

metadata

unless

it

is

for

the

purposes

of

selfpromotion

Thus

author

generated

metadata

is

perceived

as

inaccurate

at

best

and

deliberately

misleading

at

worst

-LSB-

-RSB-

Tagging

services

sidestep

this

issue

of

author

versus

expert

categorization

opting

instead

for

user

created

metadata

-LSB-

-RSB-

That

is

the

people

using

the

system

are

the

ones

who

create

tags

rather

than

system

administrators

or

content

authors

Individuals

ostensibly

create

tags

to

serve

their

own

needs

and

in

doing

so

a

consensus

vocabulary

emerges

Another

characteristic

feature

of

tagging

services

is

that

they

generally

make

a

participant

's

tags

and

resources

visible

to

other

participants

Proponents

of

tagging

services

claim

that

this

adds

a

social

incentive

to

tagging

If

a

participant

tags

a

resource

she

is

interested

in

then

she

is

helping

others

who

share

similar

interests

to

find

it

too

Proponents

also

suggest

that

this

social

aspect

of

tagging

services

acts

as

a

kind

of

feedback

mechanism

for

the

folksonomy

When

a

participant

observes

how

others

have

tagged

a

resource

they

are

more

likely

to

adopt

a

similar

tagging

vocabulary

when

describing

related

resources

In

this

context

the

folksonomy

develops

as

an

emergent

vocabulary

amongst

participants

sharing

similar

interests

-LSB-

-RSB-

While

there

is

much

that

could

be

said

about

the

benefits

and

drawbacks

of

this

particular

approach

our

purpose

here

is

not

to

critique

the

philosophy

behind

collaborative

tagging

services

Hendry

et

al.

use

the

term

amateur

bibliography

to

refer

broadly

to

collections

of

resources

compiled

by

non-professionals

for

public

use

They

point

out

that

proliferation

of

amateur

bibliographies

is

a

lasting

trend

fuelled

by

the

increasing

integration

of

the

internet

into

people

's

lives

A

large

number

of

bibliographies

and

bibliography-like

information

artefacts

are

created

by

non-professionals

-LSB-

-RSB-

While

these

lists

often

fall

short

of

the

standards

of

professional

bibliography

they

are

nevertheless

important

organizing

instruments

which

in

turn

lead

to

a

great

benefit

The

various

forms

of

amateur

bibliography

are

in

general

underappreciated

and

understudied

-LSB-

-RSB-

Tagging

services

are

a

fast

growing

subset

of

amateur

bibliographies

that

warrant

further

investigation

A

few

authors

have

published

introductory

studies

on

tagging

services

-LSB-

-RSB-

and

more

recently

a

number

of

papers

were

presented

at

the

Collaborative

Web

Tagging

Workshop

at

As

stated

by

Marlow

et

al.

-LSB-

-RSB-

however

despite

the

rapid

expansion

of

applications

that

support

tagging

of

resources

tagging

systems

are

still

not

well

studied

or

understood

In

this

paper

we

examine

user

interfaces

for

tagging

services

with

a

particular

focus

on

the

enterprise

context

Tagging

in

the

enterprise

Given

the

social

aspects

of

tagging

systems

there

is

clearly

potential

for

these

services

to

support

knowledge

sharing

and

collaborative

work

within

the

enterprise

The

sharing

of

key

resources

and

the

Journal

of

Information

Science

-LRB-

-RRB-

pp.

CILIP

DOI

Downloaded

from

at

University

of

Waikato

Library

on

May

Chartered

Institute

of

Library

and

Information

Professionals

All

rights

reserved

Not

for

commercial

use

or

unauthorized

distribution

James

Sinclair

Michael

Cardew-Hall

generation

of

emergent

vocabulary

may

well

help

decrease

the

cognitive

distance

between

people

working

in

an

organization

This

helps

to

promote

social

capital

a

key

factor

in

knowledge

sharing

-LSB-

-RSB-

Although

there

is

clear

potential

for

tagging

systems

in

the

enterprise

there

are

still

key

questions

to

address

Some

of

these

include

What

factors

are

important

to

consider

when

scaling

down

a

tagging

system

from

the

whole-ofinternet

to

small

or

medium-sized

organization

scale

-LSB-

-RSB-

How

can

we

present

folksonomy

data

in

ways

that

enhance

people

's

ability

to

locate

relevant

information

In

this

paper

we

examine

aspects

of

these

questions

with

particular

respect

to

user

interfaces

for

folksonomies

Tag

clouds

Tag

clouds

are

a

user

interface

element

commonly

associated

with

folksonomy

datasets

The

Wikipedia

encyclopaedia

contains

the

following

entry

regarding

the

tag

cloud

A

tag

cloud

-LRB-

more

traditionally

known

as

a

weighted

list

in

the

field

of

visual

design

-RRB-

is

a

visual

depiction

of

content

tags

used

on

a

website

Often

more

frequently

used

tags

are

depicted

in

a

larger

font

or

otherwise

emphasized

while

the

displayed

order

is

generally

alphabetical

Thus

both

finding

a

tag

by

alphabet

and

by

popularity

is

possible

Selecting

a

single

tag

within

a

tag

cloud

will

generally

lead

to

a

collection

of

items

that

are

associated

with

that

tag

In

essence

the

tag

cloud

translates

the

emergent

vocabulary

of

a

folksonomy

into

a

social

navigation

tool

-LSB-

-RSB-

Although

tag

clouds

are

extremely

common

among

tagging

services

the

academic

literature

-LRB-

at

least

to

our

knowledge

-RRB-

appears

to

be

largely

silent

on

the

subject

In

the

blogosphere

however

there

has

been

heated

discussion

amongst

website

design

and

information

architecture

practitioners

Jeffrey

Zeldman

-LSB-

-RSB-

initially

criticized

tag

clouds

simply

for

being

popular

However

in

the

same

article

he

described

tag

clouds

as

being

smart

and

said

that

this

smartness

is

the

reason

why

so

many

have

rushed

to

use

them

In

a

subsequent

article

he

criticized

tag

clouds

as

obscuring

useful

information

by

being

skewed

towards

what

is

popular

He

claims

that

tag

clouds

create

a

false

intellectual

equivalence

between

high

and

low-level

topics

-LSB-

-RSB-

Some

of

his

criticisms

were

not

so

much

of

tag

clouds

however

as

folksonomies

in

general

Some

bloggers

such

as

Jon

-LSB-

-RSB-

wrote

responses

to

these

articles

and

defended

the

tag

cloud

as

a

navigation

aid

Jon

claimed

that

the

tag

cloud

is

a

powerful

tool

that

has

yet

to

realize

its

full

potential

Others

such

as

Porter

-LSB-

-RSB-

responded

to

the

broader

criticisms

of

folksonomies

in

general

Zeldman

appears

to

claim

that

the

popularity

metric

hinders

access

to

useful

items

that

are

not

tagged

with

popular

keywords

Porter

counter-argues

however

that

just

because

an

item

is

popular

that

does

not

necessarily

mean

it

is

irrelevant

Notably

Tomas

Vander

Wal

the

man

credited

with

coining

the

term

folksonomy

-LSB-

-RSB-

described

tag

clouds

as

being

cute

but

offer

-LSB-

ing

-RSB-

little

value

-LSB-

-RSB-

While

the

discussion

in

the

blogosphere

has

been

lively

very

few

of

the

authors

have

performed

empirical

studies

to

verify

their

claims

As

Brooks

and

Montanez

point

out

-LSB-

M

-RSB-

uch

of

the

discussion

regarding

the

advantages

and

disadvantages

of

tags

and

folksonomy

has

taken

place

within

the

blogosphere

as

opposed

to

within

peer-reviewed

conferences

or

journals

The

blogosphere

has

the

great

advantage

of

allowing

this

discussion

to

happen

quickly

and

provide

a

voice

to

all

interested

participants

but

it

also

presents

a

difficult

challenge

to

researchers

in

terms

of

properly

evaluating

and

acknowledging

contributions

that

have

not

been

externally

vetted

-LSB-

-RSB-

This

lack

of

empirical

evidence

prompted

our

investigation

of

the

advantages

and

disadvantages

of

tag

clouds

for

exploring

folksonomy

data

This

fits

in

with

our

broader

aim

of

investigating

the

use

of

tagging

systems

in

small

to

medium-sized

organizations

Journal

of

Information

Science

-LRB-

-RRB-

pp.

CILIP

DOI

Downloaded

from

at

University

of

Waikato

Library

on

May

Chartered

Institute

of

Library

and

Information

Professionals

All

rights

reserved

Not

for

commercial

use

or

unauthorized

distribution

James

Sinclair

Michael

Cardew-Hall

Table

Participants

Participants

Female

Male

Age

range

Participants

with

a

blog

Participants

who

use

a

social

book-marking

service

-LRB-

e.g.

Delicious

Furl

Magnolia

-RRB-

Articles

tagged

Mean

articles

tagged

per

session

Mean

articles

tagged

per

participant

The

study

The

question

we

addressed

here

is

Do

tag

clouds

provide

value

to

people

seeking

information

from

a

folksonomy

dataset

To

answer

this

question

we

created

a

folksonomy-like

dataset

and

asked

participants

to

perform

information-seeking

tasks

on

it

We

provided

participants

with

the

option

of

using

either

a

tag

cloud

or

a

search

box

interface

to

query

the

database

Our

hypothesis

was

that

if

people

found

no

value

in

a

tag

cloud

for

information

seeking

then

they

would

primarily

use

the

search

box

to

seek

information

At

best

people

would

attempt

one

or

two

queries

using

the

tag

cloud

then

resort

to

the

search

box

We

found

that

while

some

participants

preferred

to

use

the

search

box

exclusively

a

significant

proportion

of

participants

used

the

tag

cloud

to

find

information

In

total

the

number

of

questions

answered

using

the

tag

cloud

was

greater

than

those

answered

using

the

search

box

Thus

we

believe

that

the

tag

cloud

does

provide

value

for

navigating

a

folksonomy

dataset

Examining

survey

comments

and

usage

patterns

of

the

data

we

explore

the

benefits

and

limitations

of

the

tag

cloud

Method

A

key

characteristic

of

the

folksonomy

is

that

it

is

user

created

metadata

-LSB-

-RSB-

that

is

the

people

who

use

the

system

are

also

the

ones

tagging

items

Hence

instead

of

downloading

an

existing

folksonomy

dataset

for

use

in

this

study

-LSB-

as

with

other

studies

such

as

-RSB-

we

asked

participants

in

the

study

to

tag

articles

from

Slashdot

to

create

a

folksonomy-like

dataset

This

ensured

that

the

participants

querying

the

dataset

had

also

contributed

tags

to

the

dataset

To

conduct

the

experiment

we

recruited

participants

from

students

enrolled

in

a

first-year

engineering

class

offered

by

the

Department

of

Engineering

at

the

Australian

National

University

Table

summarizes

other

characteristics

of

the

participants

In

the

experiment

we

asked

participants

to

tag

around

articles

each

using

the

interface

shown

in

Figure

The

system

selected

articles

randomly

from

a

pool

of

articles

dated

from

August

to

May

Explaining

the

experiment

and

allowing

participants

to

tag

articles

took

approximately

minutes

for

each

session

The

system

allowed

participants

to

enter

as

many

tags

as

they

wished

separated

by

commas

This

made

it

possible

to

use

spaces

in

tags

rather

than

restricting

the

participant

to

a

single

word

This

is

in

contrast

to

many

popular

tagging

systems

such

as

Delicious

and

Flickr

which

only

allow

single

word

tags

We

allowed

the

use

of

multi-word

tags

to

eliminate

the

problem

of

establishing

a

convention

for

word

combination

One

study

found

that

at

least

of

tags

in

Delicious

were

compound

words

created

by

putting

a

symbol

or

piece

of

punctuation

inside

the

tag

to

represent

a

space

yet

no

consensus

or

convention

had

been

chosen

by

the

Delicious

user

community

-LSB-

-RSB-

Allowing

spaces

-LRB-

in

a

manner

similar

to

that

adopted

by

-RRB-

avoids

this

issue

Journal

of

Information

Science

-LRB-

-RRB-

pp.

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DOI

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and

Information

Professionals

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reserved

Not

for

commercial

use

or

unauthorized

distribution

James

Sinclair

Michael

Cardew-Hall

Fig.

The

interface

for

tagging

articles

The

tagging

interface

also

listed

all

the

tags

a

participant

had

used

to

date

on

the

right-hand

side

of

the

screen

under

the

heading

My

tags

Clicking

on

one

of

the

tags

added

the

tag

to

the

form

at

the

bottom

of

the

screen

This

is

a

timesaving

feature

common

in

tagging

services

such

as

Delicious

and

CiteULike

We

included

this

feature

to

encourage

consistency

in

tagging

by

reminding

participants

of

tags

they

had

used

previously

For

example

a

participant

might

enter

web

design

for

one

article

and

website

design

for

another

We

included

the

My

tags

to

limit

this

individual

non-conformity

Each

session

was

divided

into

two

halves

with

the

first

half

being

devoted

to

tagging

articles

and

the

second

half

devoted

to

querying

the

database

of

tagged

articles

In

the

second

half

we

asked

participants

to

answer

questions

-LRB-

see

Table

-RRB-

by

querying

the

collection

of

tagged

articles

The

questions

were

designed

to

elicit

a

range

of

information-seeking

behaviour

ranging

from

general

browsing

to

specific

information

seeking

Questions

and

were

the

most

general

giving

no

specific

topic

to

search

on

while

questions

and

related

to

specific

topics

represented

by

a

relatively

small

number

of

articles

in

the

dataset

To

query

the

database

we

presented

participants

with

the

option

of

using

either

a

tag

cloud

or

a

conventional

search

box

Both

options

were

presented

on

the

same

screen

with

approximately

half

of

the

horizontal

screen

space

being

given

to

each

-LRB-

see

Figure

-RRB-

The

system

recorded

which

interface

participants

used

to

make

their

queries

and

which

question

was

displayed

on

the

screen

at

the

time

The

tag

cloud

displayed

the

top

most-used

tags

in

the

database

As

folksonomy

frequency

tends

to

follow

a

power

law

distribution

-LSB-

-RSB-

we

made

the

size

of

each

tag

proportional

to

the

log

of

its

frequency

The

specific

equation

used

was

TagSize

C

log

-LRB-

fi

fmin

-RRB-

log

-LRB-

fmax

fmin

-RRB-

-LRB-

-RRB-

where

fi

is

the

frequency

of

tag

i

fmin

is

the

minimum

frequency

in

the

top

tags

fmax

is

the

maximum

frequency

in

the

top

tags

and

C

is

a

scaling

constant

that

determines

the

maximum

text

size

This

gives

a

tag

size

relative

to

the

default

font

size

set

by

the

browser

Clicking

on

a

tag

returned

a

list

of

all

articles

tagged

with

that

keyword

The

results

page

for

the

tag

cloud

interface

showed

a

subsequent

tag

cloud

for

all

the

articles

featuring

the

selected

tag

-LRB-

Figure

-RRB-

Clicking

on

a

tag

in

this

sub-cloud

would

further

refine

the

results

to

articles

containing

both

tags

and

so

on

for

sub-sub-clouds

etc.

Journal

of

Information

Science

-LRB-

-RRB-

pp.

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DOI

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Institute

of

Library

and

Information

Professionals

All

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reserved

Not

for

commercial

use

or

unauthorized

distribution

James

Sinclair

Michael

Cardew-Hall

Table

Questions

asked

of

participants

to

elicit

information-seeking

behaviour

Tell

me

about

an

interesting

application

of

nanotechnology

Paste

the

title

of

an

article

you

find

interesting

Paste

the

title

of

an

article

that

contains

something

about

Mars

Paste

the

title

of

an

article

you

find

amusing

Paste

the

title

of

an

article

related

to

biology

Tell

me

something

about

sustainable

fuels

Tell

me

about

a

scientific

idea/discovery

that

has

military

applications

Paste

the

title

of

an

article

related

to

NASA

Paste

the

title

of

an

article

related

to

archaeology

Paste

the

title

of

an

article

related

to

video

game

addiction

Fig.

The

search

interface

showing

the

search

box

and

tag

cloud

This

particular

screenshot

is

from

the

third

session

The

system

generated

query

results

for

the

search

box

using

the

Ranking

with

Vector

Spaces

algorithm

provided

by

MySQL

using

the

full

text

of

each

article

This

algorithm

uses

a

variation

on

the

well

known

TF-IDF

term-weighting

method

In

this

method

the

database

system

assigns

each

term

in

the

collection

a

weight

according

to

its

frequency

in

the

collection

and

its

frequency

in

each

article

When

a

participant

performs

a

query

the

algorithm

selects

and

ranks

articles

according

to

the

matched

query

terms

and

their

individual

weights

By

contrast

query

results

from

the

tag

cloud

were

generated

by

simply

retrieving

all

articles

tagged

with

the

chosen

keyword

That

is

any

article

tagged

with

the

relevant

keyword

was

included

in

the

search

results

This

left

the

question

of

how

to

rank

the

search

results

however

In

order

to

maintain

consistency

we

ranked

the

result

set

displayed

by

the

tag

cloud

using

the

same

Ranking

with

Vector

Spaces

algorithm

used

for

the

search

box

So

while

the

methods

used

to

select

which

articles

to

display

were

different

for

the

search

box

and

tag

cloud

the

same

method

was

used

to

rank

the

displayed

results

While

most

folksonomy-based

websites

order

results

by

how

recently

items

were

tagged

and

the

number

of

people

tagging

the

item

in

this

system

such

an

organization

would

have

had

little

relevance

Other

methods

of

ranking

folksonomy

search

results

have

been

proposed

e.g.

-LSB-

-RSB-

however

Journal

of

Information

Science

-LRB-

-RRB-

pp.

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DOI

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Library

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Professionals

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Not

for

commercial

use

or

unauthorized

distribution

James

Sinclair

Michael

Cardew-Hall

Fig.

Example

of

results

from

a

tag

cloud

query

Fig.

The

exit

survey

our

intention

was

not

to

compare

the

precision

and

recall

of

the

two

techniques

but

rather

to

examine

user

perceptions

and

usage

patterns

Hence

we

attempted

to

maintain

consistency

by

ranking

results

using

the

same

algorithm

for

both

query

methods

Journal

of

Information

Science

-LRB-

-RRB-

pp.

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DOI

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Professionals

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Not

for

commercial

use

or

unauthorized

distribution

James

Sinclair

Michael

Cardew-Hall

After

completing

questions

the

system

presented

participants

with

an

exit

survey

asking

two

questions

-LRB-

see

Figure

-RRB-

Characteristics

of

the

dataset

The

folksonomy

dataset

created

through

this

process

is

quite

different

to

that

of

frequently

studied

large-scale

systems

such

as

Flickr

or

Delicious

The

experimental

conditions

here

however

reflect

contexts

that

often

occur

in

an

enterprise

context

Not

all

organizations

have

thousands

of

employees

Managers

often

delegate

data

entry

and

information

management

tasks

to

subordinates

There

is

potential

in

many

organizational

settings

for

people

to

be

tagging

resources

that

they

did

not

select

without

any

personal

benefit

to

themselves

Of

course

such

situations

would

not

be

ideally

suited

to

a

folksonomy

in

practice

however

all

methods

of

categorization

are

less

than

ideal

-LSB-

-RSB-

The

specific

differences

in

contrast

to

a

larger-scale

folksonomy

are

as

follows

The

number

of

participants

was

relatively

small

when

compared

with

studies

of

tagging

systems

in

the

enterprise

-LSB-

such

as

-RSB-

which

report

upwards

of

participants

Participants

did

not

select

articles

to

tag

This

is

in

contrast

to

most

tagging

services

where

participants

select

for

themselves

which

resources

they

wish

to

tag

This

provides

a

kind

of

quality

filter

not

present

in

the

experiment

we

conducted

As

the

shared

dataset

was

artificially

selected

participants

lacked

a

context

for

tagging

Participants

in

most

tagging

systems

find

some

kind

of

personal

benefit

for

tagging

Delicious

for

example

allows

participants

to

better

organize

their

own

bookmarks

Thus

tags

created

in

this

context

have

personal

relevance

for

the

participant

In

this

case

participants

were

tagging

simply

because

it

was

part

of

the

experiment

There

was

no

social

feedback

aspect

to

tagging

Unlike

most

tagging

systems

participants

were

not

able

to

browse

others

tags

in

this

experiment

Since

participants

did

not

select

which

articles

to

tag

this

would

have

had

little

relevance

However

this

prevented

participants

from

observing

how

others

tagged

articles

potentially

resulting

in

less

convergence

on

tag

usage

These

conditions

produce

something

of

a

worst-case-scenario

folksonomy

However

if

a

tag

cloud

shows

value

for

navigation

in

a

less-than-ideal

dataset

such

as

this

then

we

assume

that

it

will

perform

even

better

with

more

typical

folksonomy

data

Results

Last

strike

queries

Do

tag

clouds

provide

value

to

people

seeking

information

from

a

folksonomy

dataset

To

examine

this

question

we

assume

that

the

last

query

made

before

answering

a

question

provided

the

relevant

information

to

answer

We

shall

refer

to

the

last

query

made

before

answering

as

a

last-strike

query

In

practice

this

assumption

about

last-strike

queries

may

not

always

hold

true

Someone

may

continue

querying

a

database

even

after

they

have

found

a

relevant

resource

simply

to

see

if

a

better

resource

is

available

In

the

context

of

this

study

however

the

majority

of

questions

asked

participants

to

copy

and

paste

from

articles

This

makes

it

more

likely

that

the

last-strike

query

did

indeed

provide

the

relevant

answer

Each

of

the

participants

answered

questions

giving

last-strike

queries

We

assume

that

those

who

did

not

search

to

answer

a

question

either

answered

from

their

own

knowledge

or

used

the

results

from

the

previous

search

Table

shows

that

participants

answered

the

majority

of

questions

using

the

tag

cloud

Thus

it

seems

that

the

tag

cloud

does

provide

some

kind

of

value

Examining

the

last

strike

queries

for

each

question

however

-LRB-

see

Figure

-RRB-

participants

favoured

the

search

box

for

six

of

the

questions

Investigating

this

further

we

find

two

scenarios

where

use

of

the

tag

cloud

outweighed

use

of

the

Journal

of

Information

Science

-LRB-

-RRB-

pp.

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DOI

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from

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of

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of

Library

and

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Professionals

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reserved

Not

for

commercial

use

or

unauthorized

distribution

James

Sinclair

Michael

Cardew-Hall

Table

Last-strike

queries

Method

Last-strike

queries

Tag

cloud

-LRB-

-RRB-

Search

box

-LRB-

-RRB-

Did

not

search

-LRB-

-RRB-

Last

Strike

Query

Methods

by

Question

Participants

Question

Did

not

search

Fig.

Query

method

used

to

answer

each

question

Search

Box

Tag

Cloud

search

box

The

first

was

where

the

information-seeking

task

was

broad

and

non-specific

as

in

questions

and

The

second

was

when

the

tag

cloud

contained

a

keyword

relevant

to

the

question

Browsing

versus

finding

Questions

and

were

broad

non-specific

questions

Paste

the

title

of

an

article

you

find

interesting

Paste

the

title

of

an

article

you

find

amusing

The

number

of

last

strike

queries

for

the

tag

cloud

was

much

greater

than

the

search

box

for

both

these

questions

This

suggests

that

tag

clouds

are

useful

when

the

information-seeking

task

is

nonspecific

That

is

tag

clouds

support

browsing

or

serendipitous

discovery

Other

authors

have

observed

that

this

is

a

feature

of

folksonomies

in

general

Mathes

-LSB-

-RSB-

commented

that

one

of

the

strengths

of

folksonomies

is

serendipity

Browsing

the

system

and

its

interlinked

related

tag

sets

is

wonderful

for

finding

things

unexpectedly

in

a

general

area

Brooks

and

Montanez

-LSB-

-RSB-

also

found

that

tagging

is

most

effective

at

placing

articles

into

broad

categories

but

is

less

effective

as

a

tool

for

indicating

an

article

's

specific

content

Journal

of

Information

Science

-LRB-

-RRB-

pp.

CILIP

DOI

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from

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Library

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Not

for

commercial

use

or

unauthorized

distribution

James

Sinclair

Michael

Cardew-Hall

Table

Mean

queries

to

answer

question

where

participants

relied

on

a

single

interface

No

keyword

in

cloud

Search

Box

Tag

Cloud

Keyword

in

cloud

Search

Box

300 250 200

Frequency

Number

of

Queries

to

Answer

Question

Fig.

Frequency

300 250 200 150

Tag

Cloud

Number

of

Queries

to

Answer

Question

Number

of

queries

required

to

answer

questions

where

participants

relied

solely

on

a

single

interface

In

addition

participants

comments

from

the

exit

survey

also

support

this

observation

Eight

respondents

indicated

that

their

choice

of

interface

depended

on

the

information-seeking

task

often

commenting

that

the

tags

supported

more

general

browsing

Ten

participants

commented

that

the

search

box

allowed

for

greater

specificity

when

entering

queries

Examples

included

-LSB-

-RSB-

the

tags

were

more

usefull

-LSB-

sic

-RSB-

for

finding

things

about

general

topics

-LSB-

-RSB-

the

search

bar

was

better

for

spicific

-LSB-

sic

-RSB-

things

-LSB-

-RSB-

the

search

box

lets

you

find

exactly

what

you

want

however

if

you

wanted

to

find

related

articles

the

tags

are

more

useful

If

searching

for

something

specific

you

can

specify

more

constraints

about

the

subject

so

less

results

are

returned

otherwise

for

more

general

use

the

tags

would

be

good

Conversely

it

seems

that

the

tag

cloud

was

less

useful

when

the

information-seeking

task

was

more

specific

In

general

it

appears

that

answering

a

question

using

the

tag

cloud

required

more

queries

per

question

than

the

search

box

We

can

not

directly

compare

the

mean

number

of

searches

for

each

interface

however

since

a

participant

may

have

used

a

mixture

of

both

methods

To

compare

number

of

searches

to

find

an

answer

we

examine

the

cases

where

a

participant

solely

relied

on

just

one

interface

to

answer

a

question

The

histograms

for

both

interfaces

-LRB-

Figure

-RRB-

show

that

most

questions

were

answered

with

a

single

query

In

general

however

participants

who

relied

solely

on

the

tag

cloud

made

more

queries

when

answering

a

question

even

when

a

relevant

keyword

was

present

in

the

tag

cloud

-LRB-

see

Table

-RRB-

The

tail

of

the

tag

cloud

histogram

in

Figure

shows

two

cases

where

participants

made

to

queries

before

answering

a

question

This

suggests

that

participants

who

used

the

tag

cloud

were

more

likely

to

engage

in

browsing

behaviour

Journal

of

Information

Science

-LRB-

-RRB-

pp.

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Sinclair

Michael

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Table

Last

strike

queries

when

relevant

keywords

were

present

in

tag

cloud

Keyword

in

tag

cloud

Did

not

search

Search

Box

Tag

Cloud

65 194 194

No

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

31 173 233

Yes

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

Table

Participants

preferences

from

the

exit

survey

Search

-LRB-

-RRB-

Do

n't

know

-LRB-

-RRB-

Tag

-LRB-

-RRB-

Did

not

answer

-LRB-

-RRB-

Presence

of

relevant

keywords

in

the

tag

cloud

The

other

questions

where

use

of

the

tag

cloud

dominated

were

Questions

and

These

were

more

specific

questions

than

and

referring

to

a

particular

subject

or

topic

Paste

the

title

of

an

article

related

to

biology

Paste

the

title

of

an

article

related

to

NASA

With

these

questions

however

there

was

a

relevant

keyword

present

in

the

tag

cloud

for

the

majority

of

sessions

The

keyword

NASA

was

present

in

the

tag

cloud

for

every

session

The

keyword

biology

was

present

in

the

tag

cloud

for

seven

of

the

nine

sessions

When

present

in

the

tag

cloud

both

keywords

were

relatively

large

in

proportion

to

the

surrounding

text

A

preference

for

the

tag

cloud

with

these

questions

makes

sense

since

clicking

on

a

single

keyword

requires

less

effort

than

typing

search

terms

and

submitting

a

form

Investigating

this

further

we

find

that

last-strike

usage

of

the

tag

cloud

increased

whenever

there

was

a

relevant

keyword

present

in

the

tag

cloud

-LRB-

see

Table

-RRB-

This

is

consistent

with

participant

comments

that

clicking

on

a

single

keyword

was

faster

than

typing

it

into

the

search

box

For

example

It

was

quicker

instead

of

typing

in

a

search

word/s

you

can

just

click

on

the

provided

keywords

to

find

a

relevant

article

-LSB-

-RSB-

clicking

is

fast

-LSB-

er

-RSB-

than

typing

-LSB-

-RSB-

When

the

question

had

a

tag

associated

with

it

-LSB-

it

-RSB-

was

faster

to

use

the

tag

Participants

preference

In

the

exit

survey

a

majority

of

participants

stated

a

preference

for

the

search

box

-LRB-

Table

-RRB-

Participants

reasons

for

their

preference

were

coded

and

tabulated

as

shown

in

Table

There

was

a

wide

variety

of

responses

however

the

top

three

reasons

are

particularly

interesting

since

they

reflect

our

findings

regarding

browsing

versus

finding

A

factor

to

consider

here

is

the

makeup

of

the

participant

group

As

mentioned

previously

we

recruited

participants

from

a

first-year

engineering

class

Amongst

such

a

group

there

is

likely

to

be

a

high

level

of

computer

literacy

and

familiarity

with

information

seeking

tools

such

as

search

engines

At

the

same

time

very

few

of

the

participants

appeared

to

be

familiar

with

the

concept

of

tagging

or

tag

clouds

Only

two

participants

indicated

that

they

used

a

social

bookmarking

service

Journal

of

Information

Science

-LRB-

-RRB-

pp.

CILIP

DOI

Downloaded

from

at

University

of

Waikato

Library

on

May

Chartered

Institute

of

Library

and

Information

Professionals

All

rights

reserved

Not

for

commercial

use

or

unauthorized

distribution

James

Sinclair

Michael

Cardew-Hall

Table

Reasons

for

choosing

one

interface

over

another

Responses

Reason

category

Search

box

allowed

greater

specificity

Depends

on

information-seeking

task

The

tag

cloud

was

faster

The

search

box

was

faster

The

tag

cloud

suggests

useful

query

terms

The

search

box

was

easier

to

use

-LRB-

or

the

tag

cloud

was

more

difficult

-RRB-

The

search

box

is

familiar

or

the

tag

cloud

unfamiliar

The

tag

cloud

was

easier

to

use

-LRB-

or

the

search

box

was

more

difficult

-RRB-

and

only

of

the

participants

indicated

that

they

kept

a

blog

This

may

have

lead

to

a

greater

preference

for

the

search

box

due

to

its

familiarity

Comments

from

the

exit

survey

support

this

For

example

I

am

used

to

searching

with

search

boxes

on

databases

and

the

internet

-LSB-

I

preferred

the

search

box

because

I

am

-RSB-

used

to

it

-LSB-

The

search

box

is

-RSB-

a

system

I

am

used

to

Tag

cloud

as

a

visual

summary

There

is

evidence

to

suggest

that

formulating

a

free

text

search

query

requires

greater

mental

activity

than

browsing

a

list

of

links

-LSB-

-RSB-

In

order

to

formulate

a

query

a

participant

must

recall

relevant

words

to

enter

into

the

search

box

a

cognitively

more

expensive

task

than

recognizing

relevant

words

in

a

list

-LSB-

-RSB-

A

number

of

the

comments

from

the

exit

survey

indicated

that

the

tag

cloud

suggested

useful

search

terms

saving

the

participant

some

cognitive

effort

Examples

of

participant

's

comments

include

-LSB-

T

-RSB-

he

tags

made

it

so

you

didn

-LSB-

-RSB-

t

need

to

think

of

what

you

wanted

to

find

The

tags

invoked

ideas

immediately

in

my

mind

making

access

easier

When

you

want

to

search

something

but

you

do

n't

know

where

to

start

using

tags

are

the

right

way

to

start

In

the

experiment

sessions

we

also

observed

that

participants

for

whom

English

was

a

second

language

seemed

to

find

the

tag

cloud

particularly

helpful

in

this

regard

This

suggests

that

tag

clouds

may

have

the

potential

to

increase

accessibility

for

people

performing

information-seeking

tasks

in

a

second

language

More

research

is

needed

to

confirm

this

hypothesis

however

Tag

cloud

occlusion

One

disadvantage

of

the

tag

cloud

is

that

when

used

in

this

manner

it

does

not

allow

direct

access

to

all

articles

in

the

database

When

the

system

presented

search

results

from

a

tag

based

query

it

also

showed

a

cloud

of

co-occurring

tags

as

displayed

in

Figure

Clicking

on

one

of

these

co-occurring

tags

presented

articles

tagged

with

both

keywords

Thus

if

an

article

had

no

tags

in

the

front

tag

cloud

-LRB-

that

is

in

the

most

popular

tags

-RRB-

then

it

was

inaccessible

from

the

tag

cloud

interface

In

every

session

of

the

experiment

a

significant

number

of

articles

were

not

accessible

from

the

tag

cloud

Interestingly

while

the

number

of

tags

in

the

cloud

remained

constant

-LRB-

tags

-RRB-

the

number

of

occluded

articles

increased

proportionally

to

the

total

number

of

articles

-LRB-

see

Figure

-RRB-

The

percentage

of

occlusion

remained

roughly

constant

at

about

for

each

session

-LRB-

see

Figure

-RRB-

This

inability

to

make

all

articles

accessible

from

a

single

page

is

a

major

disadvantage

compared

with

the

search

box

Hence

tag

clouds

are

clearly

not

sufficient

as

the

sole

means

of

navigating

a

folksonomy

dataset

Journal

of

Information

Science

-LRB-

-RRB-

pp.

CILIP

DOI

Downloaded

from

at

University

of

Waikato

Library

on

May

Chartered

Institute

of

Library

and

Information

Professionals

All

rights

reserved

Not

for

commercial

use

or

unauthorized

distribution

James

Sinclair

Michael

Cardew-Hall

Occluded

Articles

Occluded

Articles

500 400 300 200

200 400 600

Total

Articles

Tagged

Fig.

Articles

inaccessible

from

the

tag

cloud

800 1000 1200

Percentage

Occlusion

Session

Fig.

Percentage

of

articles

not

accessible

from

the

tag

cloud

Conclusion

This

paper

reports

on

an

attempt

to

answer

the

question

do

tag

clouds

provide

value

to

people

seeking

information

from

a

folksonomy

dataset

After

observing

that

a

majority

of

questions

were

answered

using

the

tag

cloud

we

conclude

that

the

tag

cloud

does

indeed

provide

value

The

tag

cloud

has

a

number

of

positive

attributes

It

is

particularly

useful

for

browsing

or

non-specific

information

discovery

This

is

partly

because

tags

are

best

suited

to

broad

categorization

as

opposed

to

specific

characterization

of

content

The

tag

cloud

also

reduces

the

cost

of

a

query

since

a

single

click

requires

less

effort

than

typing

search

terms

and

submitting

a

form

The

tag

cloud

provides

a

visual

summary

of

the

contents

of

the

database

A

number

of

participants

commented

that

this

gave

them

an

idea

of

where

to

begin

their

information

seeking

The

tag

cloud

provides

potential

for

a

two-phase

search

process

where

-LRB-

-RRB-

browsing

activity

using

the

tag

cloud

familiarizes

the

information

seeker

with

the

domain

and

-LRB-

-RRB-

a

search

mechanism

allows

more

focussed

queries

based

on

the

information

gained

from

browsing

It

appears

that

scanning

the

tag

cloud

requires

less

cognitive

load

than

formulating

specific

query

terms

That

is

scanning

and

clicking

is

easier

than

thinking

about

what

query

terms

will

produce

the

best

result

and

typing

them

into

the

search

box

We

hypothesize

that

this

may

be

particularly

useful

for

people

seeking

information

in

a

second

language

Journal

of

Information

Science

-LRB-

-RRB-

pp.

CILIP

DOI

Downloaded

from

at

University

of

Waikato

Library

on

May

Chartered

Institute

of

Library

and

Information

Professionals

All

rights

reserved

Not

for

commercial

use

or

unauthorized

distribution

James

Sinclair

Michael

Cardew-Hall

The

tag

cloud

is

not

without

its

limitations

however

A

corollary

of

the

tag

cloud

's

suitability

for

general

browsing

is

its

unsuitability

for

seeking

specific

information

Many

of

the

participants

commented

that

the

tag

cloud

did

not

allow

them

to

narrow

their

search

enough

to

answer

the

given

questions

For

this

experiment

we

found

that

roughly

half

the

articles

in

the

dataset

were

inaccessible

from

the

tag

cloud

Most

tagging

systems

mitigate

this

by

including

tag

links

when

viewing

individual

articles

thus

exposing

some

of

the

less

popular

tags

However

this

is

not

necessarily

useful

when

someone

is

seeking

specific

information

In

light

of

these

disadvantages

it

is

clear

that

the

tag

cloud

is

not

sufficient

as

the

sole

means

of

navigating

a

folksonomy

dataset

Some

form

of

search

facility

or

supplementary

classification

scheme

is

necessary

to

expose

all

the

articles

On

the

other

hand

it

would

be

unfair

to

say

that

the

tag

cloud

offers

no

value

as

a

navigation

aid

Where

relevant

keywords

are

present

clicking

on

a

tag

is

quicker

and

easier

than

entering

query

terms

into

a

search

box

It

also

provides

a

useful

visual

summary

of

the

contents

of

the

database

Endnotes

-LRB-

accessed

July

-RRB-

See

for

details

References

-LSB-

-RSB-

O.

Burkeman

Folksonomy

The

Guardian

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Technology

-RRB-

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O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O B O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O B O O O O O O O O O O O O O O B O O O O O B O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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