Journal

of

Information

Science

Usage

patterns

of

collaborative

tagging

systems

Scott

A.

Golder

and

Bernardo

A.

Huberman

Journal

of

Information

Science

DOI

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version

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article

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reserved

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for

commercial

use

or

unauthorized

distribution

Usage

patterns

of

collaborative

tagging

systems

Scott

A.

Golder

and

Bernardo

A.

Huberman

Information

Dynamics

Lab

HP

Labs

Palo

Alto

CA

USA

Received

August

Revised

October

Abstract

Collaborative

tagging

describes

the

process

by

which

many

users

add

metadata

in

the

form

of

keywords

to

shared

content

Recently

collaborative

tagging

has

grown

in

popularity

on

the

web

on

sites

that

allow

users

to

tag

bookmarks

photographs

and

other

content

In

this

paper

we

analyze

the

structure

of

collaborative

tagging

systems

as

well

as

their

dynamic

aspects

Specifically

we

discovered

regularities

in

user

activity

tag

frequencies

kinds

of

tags

used

bursts

of

popularity

in

bookmarking

and

a

remarkable

stability

in

the

relative

proportions

of

tags

within

a

given

URL

We

also

present

a

dynamic

model

of

collaborative

tagging

that

predicts

these

stable

patterns

and

relates

them

to

imitation

and

shared

knowledge

Keywords

collaborative

tagging

Del.icio.us

bookmarks

web

sharing

folksonomy

Introduction

Marking

content

with

descriptive

terms

also

called

keywords

or

tags

is

a

common

way

of

organizing

content

for

future

navigation

filtering

or

search

Though

organizing

electronic

content

this

way

is

not

Correspondence

to

Scott

A.

Golder

HP

Labs

Page

Mill

Rd

ms

Palo

Alto

CA

USA

E-mail

scott.golder@hp.com

new

a

collaborative

form

of

this

process

which

has

been

given

the

name

tagging

by

its

proponents

is

gaining

popularity

on

the

web

Document

repositories

or

digital

libraries

often

allow

documents

in

their

collections

to

be

organized

by

assigned

keywords

However

traditionally

such

categorizing

or

indexing

is

either

performed

by

an

authority

such

as

a

librarian

or

else

derived

from

the

material

provided

by

the

authors

of

the

documents

-LSB-

-RSB-

In

contrast

collaborative

tagging

is

the

practice

of

allowing

anyone

especially

consumers

to

freely

attach

keywords

or

tags

to

content

Collaborative

tagging

is

most

useful

when

there

is

nobody

in

the

librarian

role

or

there

is

simply

too

much

content

for

a

single

authority

to

classify

both

of

these

traits

are

true

of

the

web

where

collaborative

tagging

has

grown

popular

This

kind

of

collaborative

tagging

offers

an

interesting

alternative

to

current

efforts

at

semantic

web

ontologies

-LSB-

-RSB-

which

have

been

a

focus

of

research

by

a

number

of

groups

-LRB-

e.g.

-LSB-

-RSB-

-RRB-

A

number

of

now

prominent

web

sites

feature

collaborative

tagging

Typically

such

sites

allow

users

to

publicly

tag

and

share

content

so

that

they

can

not

only

categorize

information

for

themselves

they

can

also

browse

the

information

categorized

by

others

There

are

therefore

at

once

both

personal

and

public

aspects

to

collaborative

tagging

systems

In

some

sites

collaborative

tagging

is

also

known

as

folksonomy

short

for

folk

taxonomy

however

there

is

some

debate

whether

this

term

is

accurate

-LSB-

-RSB-

and

so

we

avoid

using

it

here

Del.icio.us

the

site

on

which

we

performed

our

analysis

allows

for

the

collaborative

tagging

of

shared

website

bookmarks

-LSB-

-RSB-

Yahoo

's

MyWeb

-LSB-

-RSB-

does

this

as

well

and

CiteULike

-LSB-

-RSB-

and

Connotea

-LSB-

-RSB-

do

the

same

for

references

to

academic

publications

Some

services

allow

users

to

tag

but

only

content

they

own

for

example

Technorati

-LSB-

-RSB-

allows

one

to

tag

one

's

weblog

posts

Though

some

sites

do

not

strictly

speaking

Journal

of

Information

Science

-LRB-

-RRB-

pp.

CILIP

DOI

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Library

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Not

for

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use

or

unauthorized

distribution

S.A.

GOLDER

AND

B.A.

HUBERMAN

support

collaborative

tagging

we

mention

this

one

to

illustrate

the

growth

of

tagging

in

a

variety

of

media

In

this

paper

we

analyze

the

structure

of

collaborative

tagging

systems

as

well

as

their

dynamic

aspects

Specifically

through

the

study

of

the

collaborative

tagging

system

Del.icio.us

we

are

able

to

discover

regularities

in

user

activity

tag

frequencies

kinds

of

tags

used

and

bursts

of

popularity

in

bookmarking

as

well

as

a

remarkable

stability

in

the

relative

proportions

of

tags

within

a

given

URL

We

also

present

a

dynamic

model

of

collaborative

tagging

that

predicts

these

stable

patterns

and

relates

them

to

imitation

and

shared

knowledge

We

conclude

with

a

discussion

of

potential

uses

of

the

data

that

users

of

these

systems

collaboratively

generate

Tagging

and

taxonomy

Proponents

of

collaborative

tagging

typically

in

the

weblogging

community

often

contrast

tagging-based

systems

with

taxonomies

While

the

latter

are

hierarchical

and

exclusive

the

former

are

non-hierarchical

and

inclusive

Familiar

taxonomies

include

the

Linnaean

system

of

classifying

living

things

the

Dewey

Decimal

classification

for

libraries

and

computer

file

systems

for

organizing

electronic

files

In

such

systems

each

animal

book

file

and

so

on

is

in

one

unambiguous

category

which

is

in

turn

within

a

yet

more

general

one

For

example

lions

and

tigers

fall

in

the

genus

panthera

and

domestic

cats

in

the

genus

felis

but

panthera

and

felis

are

both

part

of

family

felidae

of

which

lions

tigers

and

domestic

cats

are

all

part

Similarly

books

on

Africa

's

geography

are

in

the

Dewey

Decimal

system

category

and

books

on

South

America

's

in

but

both

are

subsumed

by

the

category

covering

all

topics

in

geography

In

contrast

tagging

is

neither

exclusive

nor

hierarchical

and

therefore

can

in

some

circumstances

have

an

advantage

over

hierarchical

taxonomies

For

example

consider

a

hypothetical

researcher

who

downloads

an

article

about

cat

species

native

to

Africa

If

the

researcher

wanted

to

organize

all

her

downloaded

articles

in

a

hierarchy

of

folders

there

are

several

hypothetical

options

of

which

we

consider

four

all

articles

on

cats

-LRB-

-RRB-

c

articles

cats

all

articles

on

Africa

-LRB-

-RRB-

c

articles

africa

all

articles

on

African

cats

-LRB-

-RRB-

c

articles

africa

cats

all

articles

on

cats

from

-LRB-

-RRB-

c

articles

cats

africa

Africa

Each

choice

reflects

a

decision

about

the

relative

importance

of

each

characteristic

Folder

names

and

levels

are

in

themselves

informative

in

that

like

tags

they

describe

the

information

held

within

them

-LSB-

-RSB-

Folders

like

-LRB-

-RRB-

and

-LRB-

-RRB-

make

central

the

fact

that

the

folders

are

about

cats

and

africa

respectively

but

elide

all

information

about

the

other

category

Folders

-LRB-

-RRB-

and

-LRB-

-RRB-

organize

the

files

by

both

categories

but

establish

the

first

as

primary

or

more

salient

and

the

second

as

secondary

or

more

specific

However

looking

in

-LRB-

-RRB-

for

a

file

in

-LRB-

-RRB-

will

be

fruitless

and

so

checking

multiple

locations

becomes

necessary

Despite

these

limitations

there

are

several

good

reasons

to

impose

such

a

hierarchy

Though

there

can

be

too

many

folders

in

a

hierarchy

especially

one

created

haphazardly

an

efficiently

organized

file

hierarchy

neatly

and

unambiguously

bounds

a

folder

's

contents

Unlike

a

keyword-based

search

wherein

the

seeker

can

not

be

sure

that

a

query

has

returned

all

relevant

items

a

folder

hierarchy

assures

the

seeker

that

all

the

files

it

contains

are

in

one

stable

place

In

contrast

to

a

hierarchical

file

system

a

non-exclusive

flat

tagging

system

could

unlike

the

system

described

above

identify

such

an

article

as

being

about

a

great

variety

of

things

simultaneously

including

africa

and

cats

as

well

as

animals

more

generally

and

cheetahs

more

specifically

Like

a

Venn

diagram

the

set

of

all

the

items

marked

cats

and

those

marked

africa

would

intersect

in

precisely

one

way

namely

those

documents

that

are

tagged

as

being

about

African

cats

Even

this

is

not

perfect

however

For

example

a

document

tagged

only

cheetah

would

not

be

found

in

the

intersection

of

africa

and

cats

though

it

arguably

ought

to

be

like

the

foldering

example

above

a

seeker

may

still

need

to

search

multiple

locations

Looking

at

it

another

way

tagging

is

like

filtering

out

of

all

the

possible

documents

-LRB-

or

other

items

-RRB-

that

are

tagged

a

filter

-LRB-

i.e.

a

tag

-RRB-

returns

only

those

items

tagged

with

that

tag

Depending

on

the

implementation

and

query

a

tagging

system

can

instead

of

providing

the

intersection

of

tags

-LRB-

thus

filtering

-RRB-

provide

the

union

of

tags

that

is

all

the

items

tagged

with

any

of

the

given

tags

rather

than

all

of

them

From

a

user

perspective

navigating

a

tag

system

is

similar

to

conducting

keyword-based

searches

regardless

of

the

implementation

users

are

providing

salient

descriptive

terms

in

order

to

retrieve

a

set

of

applicable

items

Semantic

and

cognitive

aspects

of

classification

Both

tagging

systems

and

taxonomies

are

beset

by

many

problems

that

exist

as

a

result

of

the

necessarily

imperfect

yet

natural

and

evolving

process

of

creating

Journal

of

Information

Science

-LRB-

-RRB-

pp.

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reserved

Not

for

commercial

use

or

unauthorized

distribution

Collaborative

tagging

semantic

relations

between

words

and

their

referents

Three

of

these

problems

are

polysemy

synonymy

and

basic

level

variation

A

polysemous

word

is

one

that

has

many

-LRB-

poly

-RRB-

related

senses

-LRB-

semy

-RRB-

For

example

a

window

may

refer

to

a

hole

in

the

wall

or

to

the

pane

of

glass

that

resides

within

it

-LSB-

-RSB-

In

practice

polysemy

dilutes

query

results

by

returning

related

but

potentially

inapplicable

items

Superficially

polysemy

is

similar

to

homonymy

where

a

word

has

multiple

unrelated

meanings

However

homonymy

is

less

a

problem

because

homonyms

can

be

largely

ruled

out

in

a

tagbased

search

through

the

addition

of

a

related

term

with

which

the

unwanted

homonym

would

not

appear

There

are

of

course

cases

where

homonyms

are

semantically

related

but

not

polysemous

for

example

searching

for

employment

at

Apple

may

be

problematic

because

of

conflicts

with

the

CEO

's

surname

Synonymy

or

multiple

words

having

the

same

or

closely

related

meanings

presents

a

greater

problem

for

tagging

systems

because

inconsistency

among

the

terms

used

in

tagging

can

make

it

very

difficult

for

one

to

be

sure

that

all

the

relevant

items

have

been

found

It

is

difficult

for

a

tagger

to

be

consistent

in

the

terms

chosen

for

tags

for

example

items

about

television

may

be

tagged

either

television

or

tv

This

problem

is

compounded

in

a

collaborative

system

where

all

taggers

either

need

to

widely

agree

on

a

convention

or

else

accept

that

they

must

issue

multiple

or

more

complex

queries

to

cover

many

possibilities

Synonymy

is

a

significant

problem

because

it

is

impossible

to

know

how

many

items

out

there

one

would

have

liked

one

's

query

to

have

retrieved

but

it

did

not

Fig.

A

Venn

diagram

showing

the

intersection

of

cats

and

africa

Relatedly

plurals

and

parts

of

speech

and

spelling

can

stymie

a

tagging

system

For

example

if

tags

cat

and

cats

are

distinct

then

a

query

for

one

will

not

retrieve

both

unless

the

system

has

the

capability

to

perform

such

replacements

built

into

it

Reflecting

the

cognitive

aspect

of

hierarchy

and

categorization

the

basic

level

problem

is

that

related

terms

that

describe

an

item

vary

along

a

continuum

of

specificity

ranging

from

very

general

to

very

specific

as

discussed

above

cat

cheetah

and

animal

are

all

reasonable

ways

to

describe

a

particular

entity

The

problem

lies

in

the

fact

that

different

people

may

consider

terms

at

different

levels

of

specificity

to

be

most

useful

or

appropriate

for

describing

the

item

in

question

The

basic

level

as

opposed

to

superordinate

-LRB-

more

general

-RRB-

and

subordinate

-LRB-

more

specific

-RRB-

levels

is

that

which

is

most

directly

related

to

humans

interactions

with

them

-LSB-

-RSB-

For

most

people

the

basic

level

for

felines

would

be

cat

rather

than

animal

or

siamese

or

persian

Experiments

demonstrate

that

when

asked

to

identify

dogs

and

birds

subjects

used

dog

and

bird

more

than

beagle

or

robin

and

when

asked

whether

an

item

in

a

picture

is

an

X

subjects

responded

more

quickly

when

X

was

a

basic

level

-LSB-

-RSB-

These

experiments

demonstrate

general

agreement

across

subjects

There

is

however

systematic

variation

across

individuals

in

what

constitutes

a

basic

level

Expertise

plays

a

role

in

defining

what

level

of

specificity

an

individual

treats

as

basic

For

example

in

the

bird

and

dog

experiments

subjects

expert

in

one

of

the

two

domains

demonstrated

basic

levels

that

were

at

levels

of

greater

specificity

than

those

without

domain

expertise

a

dog

expert

might

consider

beagle

a

basic

level

where

a

bird

expert

might

have

dog

and

a

bird

expert

robin

where

a

dog

expert

has

bird

-LSB-

-RSB-

The

underlying

factor

behind

this

variation

may

be

that

basic

levels

vary

in

specificity

to

the

degree

that

such

specificity

makes

a

difference

in

the

lives

of

the

individual

A

dog

expert

has

not

only

the

skill

but

also

the

need

to

differentiate

beagles

from

poodles

for

example

Like

variation

in

expertise

variations

in

other

social

or

cultural

categories

likely

yield

variations

in

basic

levels

For

the

purposes

of

tagging

systems

however

conflicting

basic

levels

can

prove

disastrous

as

documents

tagged

perl

and

javascript

may

be

too

specific

for

some

users

while

a

document

tagged

programming

may

be

too

general

for

others

Tagging

is

fundamentally

about

sensemaking

Sensemaking

is

a

process

in

which

information

is

categorized

and

labeled

and

critically

through

which

Journal

of

Information

Science

-LRB-

-RRB-

pp.

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DOI

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Not

for

commercial

use

or

unauthorized

distribution

S.A.

GOLDER

AND

B.A.

HUBERMAN

meaning

emerges

-LSB-

-RSB-

Recall

that

basic

levels

are

related

to

the

way

in

which

humans

interact

with

the

items

at

those

levels

-LSB-

-RSB-

when

one

interacts

with

the

outside

world

one

makes

sense

of

the

things

one

encounters

by

categorizing

them

and

ascribing

meaning

to

them

However

in

practice

categories

are

often

not

well

defined

and

their

boundaries

exhibit

vagueness

-LSB-

-RSB-

Items

often

lie

between

categories

or

equally

well

in

multiple

categories

The

lines

one

ultimately

draws

for

oneself

reflect

one

's

own

experiences

daily

practices

needs

and

concerns

Sensemaking

is

also

influenced

by

social

factors

-LSB-

-RSB-

Because

many

experiences

are

shared

with

others

and

may

be

nearly

universal

within

a

culture

or

community

similar

ways

of

organizing

and

sensemaking

do

result

after

all

societies

are

able

to

collectively

organize

knowledge

and

coordinate

action

Additionally

collective

sensemaking

is

subject

to

conflict

between

the

participating

actors

where

different

opinions

and

perspectives

can

clash

and

power

struggles

to

determine

the

terms

of

the

debate

can

ensue

-LSB-

-RSB-

Collective

tagging

then

has

the

potential

to

exacerbate

the

problems

associated

with

the

fuzziness

of

linguistic

and

cognitive

boundaries

As

all

taggers

contributions

collectively

produce

a

larger

classification

system

that

system

consists

of

idiosyncratically

personal

categories

as

well

as

those

that

are

widely

agreed

upon

However

there

is

also

opportunity

to

learn

from

one

another

through

sharing

and

organizing

information

Delicious

dynamics

Del.icio.us

or

Delicious

is

a

collaborative

tagging

system

for

web

bookmarks

that

its

creator

Joshua

Schachter

calls

a

social

bookmarks

manager

-LSB-

-RSB-

We

analyzed

data

from

Delicious

to

uncover

patterns

among

users

tags

and

URLs

We

briefly

describe

Delicious

and

analyze

tags

in

this

section

and

analyze

bookmarks

and

URLs

in

the

following

section

Finally

we

discuss

the

value

of

this

collaboratively

generated

data

Much

in

the

same

way

users

save

bookmarks

within

their

browsers

they

can

save

bookmarks

in

Delicious

instead

The

benefit

of

doing

so

is

that

once

one

's

bookmarks

are

on

the

web

they

are

accessible

from

any

computer

not

just

the

user

's

own

browser

This

is

helpful

if

one

uses

multiple

computers

at

home

work

school

and

so

on

and

is

touted

as

one

of

Delicious

main

features

Once

users

have

created

accounts

they

may

then

begin

bookmarking

web

pages

each

bookmark

records

the

web

page

's

URL

and

its

title

as

well

as

the

time

at

which

the

bookmark

is

created

Users

can

also

choose

to

tag

the

bookmark

with

multiple

tags

or

keywords

of

their

choice

Each

user

has

a

personal

page

on

which

their

bookmarks

are

displayed

this

page

is

located

at

On

this

page

all

the

bookmarks

the

user

has

ever

created

are

displayed

in

reverse

chronological

order

along

with

a

list

of

all

the

tags

the

user

has

ever

given

to

a

bookmark

By

selecting

a

tag

one

can

filter

one

's

bookmark

list

so

that

only

bookmarks

with

that

tag

are

displayed

Delicious

is

considered

social

because

not

only

can

one

see

one

's

own

bookmarks

one

can

also

see

all

of

every

other

user

's

bookmarks

The

front

page

of

Delicious

shows

several

of

the

most

recently

added

bookmarks

including

the

tags

given

to

them

who

created

them

and

how

many

other

people

have

that

bookmark

in

common

There

is

also

a

popular

page

which

shows

the

same

information

for

the

URLs

that

are

currently

the

most

popular

One

can

also

see

any

other

user

's

personal

page

and

filter

it

by

tag

much

in

the

way

one

can

one

's

own

Through

others

personal

pages

and

the

popular

page

users

can

get

a

sense

of

what

other

people

find

interesting

By

browsing

specific

people

and

tags

users

can

find

websites

that

are

of

interest

to

them

and

can

find

people

who

have

common

interests

This

too

is

touted

as

a

main

feature

of

Delicious

These

two

features

storage

of

personal

bookmarks

and

the

public

nature

of

those

bookmarks

are

somewhat

at

odds

with

one

another

The

data

we

present

below

confirm

that

users

bookmark

primarily

for

their

own

benefit

not

for

the

collective

good

but

may

nevertheless

constitute

a

useful

public

good

The

data

Our

analysis

was

performed

on

two

sets

of

Delicious

data

which

we

retrieved

between

the

morning

of

Friday

June

and

the

morning

of

Monday

June

Both

of

the

datasets

were

created

by

first

retrieving

public

RSS

feeds

and

then

crawling

a

portion

of

the

website

The

first

set

-LRB-

popular

-RRB-

contains

all

the

URLs

which

appeared

on

the

Delicious

popular

page

during

that

timeframe

Our

dataset

contains

all

bookmarks

ever

posted

to

each

of

those

URLs

regardless

of

time

so

that

for

each

URL

our

dataset

contains

its

complete

history

within

the

system

A

total

of

URLs

and

bookmarks

comprise

this

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

Collaborative

tagging

Our

second

dataset

-LRB-

people

-RRB-

consists

of

a

random

sample

of

users

who

posted

to

Delicious

during

the

above

timeframe

Our

dataset

contains

all

bookmarks

ever

posted

by

those

users

regardless

of

time

so

that

for

each

user

our

dataset

contains

that

user

's

complete

history

A

total

of

bookmarks

comprise

this

dataset

We

begin

by

looking

at

the

tag

use

of

individual

users

As

users

bookmark

new

URLs

they

create

tags

to

describe

them

Over

time

users

lists

of

tags

can

be

considered

descriptive

of

the

interests

they

hold

as

well

as

of

their

method

of

classifying

those

interests

First

we

look

at

users

activity

with

respect

to

their

tag

use

Next

we

examine

tags

themselves

in

greater

detail

User

activity

and

tag

quantity

As

might

be

expected

users

vary

greatly

in

the

frequency

and

nature

of

their

Delicious

use

In

our

people

dataset

there

is

only

a

weak

relationship

between

the

age

of

the

user

's

account

-LRB-

i.e.

the

time

since

they

created

the

account

-RRB-

and

the

number

of

days

on

which

they

created

at

least

one

bookmark

-LRB-

n

R

-RRB-

That

is

some

users

use

Delicious

very

frequently

and

others

less

frequently

Note

that

these

data

do

not

include

any

users

who

had

previously

used

Delicious

but

stopped

as

they

were

all

active

users

at

the

time

the

dataset

was

collected

More

interestingly

there

is

not

a

strong

relationship

between

the

number

of

bookmarks

a

user

has

created

and

the

number

of

tags

they

used

in

those

bookmarks

-LRB-

n

R

-RRB-

The

relationship

is

weak

at

the

low

end

of

the

scale

users

with

fewer

than

bookmarks

-LRB-

n

R

-RRB-

and

even

weaker

at

the

upper

end

users

with

more

than

bookmarks

-LRB-

n

R

-RRB-

Some

users

have

comparatively

large

sets

of

tags

and

other

users

have

comparatively

small

sets

-LRB-

Figures

and

-RRB-

Users

tag

lists

grow

over

time

as

they

discover

new

interests

and

add

new

tags

to

categorize

and

describe

them

Tags

may

exhibit

very

different

growth

rates

however

reflecting

how

users

interests

develop

and

change

over

time

Figure

and

b

shows

how

use

of

each

tag

increases

as

each

user

adds

more

bookmarks

over

time

For

each

user

two

of

those

tags

usages

grow

steadily

reflecting

continual

interests

tagged

in

a

consistent

way

One

tag

grows

rapidly

reflecting

a

newfound

interest

or

a

change

in

tagging

practice

It

is

possible

that

the

newly

growing

tag

represents

a

new

interest

or

category

to

the

user

Another

possibility

is

that

the

user

has

chosen

to

draw

a

new

distinction

Number

of

Unique

Tags

Used

800 700 600 500

400 300 200 100

Users

150 200 250

Fig.

The

number

of

tags

in

each

user

's

tag

list

in

decreasing

order

Comparison

of

Two

Users

1000 900 800 700

user

Tags

500 400 300 200

500 1000 1500 2000

user

Bookmarks

Fig.

Two

extreme

users

-LRB-

and

-RRB-

tag

growth

As

they

add

more

bookmarks

the

number

of

tags

they

use

increases

but

at

very

different

rates

among

their

bookmarks

which

can

prove

problematic

for

the

user

Because

sensemaking

is

a

retrospective

process

information

must

be

observed

before

one

can

establish

its

meaning

-LSB-

-RSB-

Therefore

a

distinction

may

go

unnoticed

for

a

long

time

until

it

is

finally

created

by

the

individual

who

then

continues

to

find

that

distinction

important

in

making

sense

of

future

information

Since

finding

previously

encountered

information

is

extremely

important

-LSB-

-RSB-

this

is

deeply

problematic

for

past

information

For

example

user

-LRB-

Figure

-RRB-

did

not

use

tag

until

approximately

the

Journal

of

Information

Science

-LRB-

-RRB-

pp.

CILIP

DOI

Downloaded

from

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University

of

Waikato

Library

on

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Chartered

Institute

of

Library

and

Information

Professionals

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rights

reserved

Not

for

commercial

use

or

unauthorized

distribution

S.A.

GOLDER

AND

B.A.

HUBERMAN

bookmark

If

tag

indeed

constitutes

a

new

distinction

among

the

kinds

of

items

this

user

bookmarks

though

Delicious

does

allow

users

to

alter

previous

bookmarks

it

would

be

arduous

to

reconsider

each

of

the

earlier

bookmarks

to

decide

whether

to

add

tag

to

them

Further

if

in

the

future

this

user

needs

to

filter

his

bookmarks

by

tag

then

no

bookmark

before

the

will

be

retrieved

compromising

the

practical

usefulness

of

the

tag

Figure

and

b

shows

that

users

tag

collections

like

their

interests

are

continually

growing

and

evolving

Next

we

look

at

what

functions

tags

play

in

bookmarks

Kinds

of

tags

Tagging

as

discussed

above

is

an

act

of

organizing

through

labeling

a

way

of

making

sense

of

many

discrete

varied

items

according

to

their

meaning

By

looking

at

those

tags

we

can

examine

what

kinds

of

distinctions

are

important

to

taggers

There

is

some

discussion

among

the

Delicious

tagging

community

concerning

whether

a

tag

is

properly

considered

to

be

descriptive

of

the

thing

itself

or

descriptive

of

a

category

into

which

the

thing

falls

-LSB-

-RSB-

However

we

see

no

contradiction

between

these

two

kinds

of

tag

When

a

category

is

defined

as

circumscribing

many

objects

with

a

particular

property

we

naturally

consider

each

of

those

objects

to

have

that

property

In

our

estimation

the

scope

of

the

tag

whether

it

describes

an

object

or

a

group

of

objects

is

less

interesting

than

the

function

of

a

tag

or

what

kind

of

information

it

conveys

and

how

it

is

used

Here

we

identify

several

functions

tags

perform

for

bookmarks

-LRB-

-RRB-

Identifying

what

-LRB-

or

who

-RRB-

it

is

about

Overwhelmingly

tags

identify

the

topics

of

bookmarked

items

These

items

include

common

nouns

of

many

levels

of

specificity

as

well

as

many

proper

nouns

in

the

case

of

content

discussing

people

or

organizations

-LRB-

-RRB-

Identifying

what

it

is

Tags

can

identify

what

kind

of

thing

a

bookmarked

item

is

in

addition

to

what

it

is

about

For

example

article

blog

and

book

-LRB-

-RRB-

Identifying

who

owns

it

Some

bookmarks

are

tagged

according

to

who

owns

or

created

the

bookmarked

content

Given

the

apparent

popularity

of

weblogs

among

Delicious

users

identifying

content

ownership

can

be

particularly

important

-LRB-

-RRB-

Refining

categories

Some

tags

do

not

seem

to

stand

alone

and

rather

than

establish

categories

themselves

refine

or

qualify

existing

categories

Numbers

especially

round

numbers

-LRB-

e.g.

-RRB-

can

perform

this

function

-LRB-

-RRB-

Identifying

qualities

or

characteristics

Adjectives

such

as

scary

funny

stupid

inspirational

tag

bookmarks

according

to

the

tagger

's

opinion

of

the

content

-LRB-

-RRB-

Self

reference

Tags

beginning

with

my

like

mystuff

and

mycomments

identify

content

in

terms

of

its

relation

to

the

tagger

-LRB-

-RRB-

Task

organizing

When

collecting

information

related

to

performing

a

task

that

information

might

be

tagged

according

to

that

task

in

order

to

group

that

information

together

Examples

-LRB-

a

-RRB-

Growth

Rates

of

Three

Tags

user

-LRB-

b

-RRB-

Growth

Rates

of

Three

Tags

user

Times

Tag

Has

Been

Used

Times

Tag

Has

Been

Used

350 300 250 200

1500 2000 2500 3000

3500 4000 4500

500 1000 1500 2000

2500 3000 3500

Bookmark

Bookmark

Fig.

Growth

rate

of

three

selected

tags

for

two

users

-LRB-

and

-RRB-

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

Collaborative

tagging

include

toread

jobsearch

Grouping

task-related

information

can

be

an

important

part

of

organizing

while

performing

a

task

-LSB-

-RSB-

The

tension

between

tags

that

may

be

useful

to

the

Delicious

community

at

large

and

those

useful

only

to

oneself

is

evident

here

The

first

three

are

not

necessarily

explicitly

personal

Though

identifying

what

some

item

is

or

is

about

presents

some

of

the

problems

discussed

earlier

like

basic

level

differences

what

unifies

the

first

four

functions

is

that

the

information

is

extrinsic

to

the

tagger

so

one

can

expect

significant

overlap

among

individuals

In

contrast

the

unifying

characteristic

of

the

final

three

functions

is

that

the

information

they

provide

is

relative

to

or

only

relevant

to

the

tagger

As

others

have

observed

-LSB-

-RSB-

some

tags

are

used

by

many

people

while

other

tags

are

used

by

fewer

people

For

the

reasons

described

above

those

tags

that

are

generally

meaningful

will

likely

be

used

by

many

taggers

while

tags

with

personal

or

specialized

meaning

will

likely

be

used

by

fewer

users

Users

have

a

strong

bias

toward

using

general

tags

first

In

each

bookmark

the

first

tag

used

has

the

highest

median

rank

-LRB-

i.e.

greatest

frequency

-RRB-

and

successive

tags

generally

have

a

decreasing

median

rank

i.e.

decreasing

frequency

-LRB-

Figure

-RRB-

Earlier

in

the

discussion

of

basic

levels

one

study

showed

that

basic

levels

were

those

that

were

most

quickly

identified

and

most

generally

agreed

upon

We

suggest

therefore

that

the

earlier

tags

in

a

bookmark

represent

basic

levels

because

they

are

not

only

widespread

in

agreement

but

are

also

the

first

terms

that

users

thought

of

when

tagging

the

URLs

in

question

A

system

seeking

to

make

use

of

this

data

in

order

to

establish

the

most

broad

categories

might

therefore

not

only

look

at

the

tags

that

are

overall

most

popular

but

also

at

those

that

are

used

earliest

within

bookmarks

Bookmarks

We

turn

our

attention

to

URLs

the

bookmarks

that

reference

them

and

the

tags

that

describe

them

Here

we

look

at

how

URLs

are

bookmarked

over

time

and

at

how

the

sets

of

tags

in

a

URL

's

bookmarks

constitute

a

stable

way

of

describing

a

URL

's

content

Trends

in

bookmarking

It

has

been

observed

elsewhere

-LSB-

-RSB-

that

URLs

often

receive

most

of

their

bookmarks

very

quickly

the

rate

of

new

bookmarks

decreasing

over

time

While

true

this

tells

only

part

of

the

story

While

many

URLs

-LRB-

e.g.

as

in

Figure

-RRB-

do

indeed

reach

their

peak

of

popularity

as

soon

as

they

reach

Delicious

many

other

URLs

-LRB-

e.g.

as

in

Figure

-RRB-

have

relatively

few

bookmarks

for

Medium

Rank

Nth

Tag

Fig.

As

a

tag

's

order

in

a

bookmark

-LRB-

horizontal

-RRB-

increases

its

rank

-LRB-

i.e.

frequency

-RRB-

in

the

list

of

tags

-LRB-

vertical

-RRB-

decreases

This

pattern

is

shown

here

for

two

URLs

-LRB-

and

-RRB-

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

S.A.

GOLDER

AND

B.A.

HUBERMAN

-LRB-

a

-RRB-

180 160 140

-LRB-

b

-RRB-

Bookmarks

Received

Day

Bookmarks

Received

Day

Fig.

The

addition

of

bookmarks

to

two

URLs

-LRB-

and

-RRB-

over

time

URL

-LRB-

a

-RRB-

peaks

immediately

whereas

-LRB-

b

-RRB-

is

obscure

for

a

long

time

before

peaking

a

long

time

until

they

are

rediscovered

and

then

experience

a

rapid

jump

in

popularity

Of

the

popular

URLs

in

our

dataset

-LRB-

-RRB-

reached

their

peak

popularity

in

their

first

days

in

Delicious

of

which

-LRB-

-RRB-

on

their

first

day

However

at

the

other

end

of

the

spectrum

another

-LRB-

-RRB-

were

in

the

Delicious

system

for

at

least

six

months

before

reaching

their

peak

of

popularity

The

URL

in

our

sample

that

took

the

longest

amount

of

time

to

peak

in

popularity

did

not

do

so

until

it

had

been

in

the

system

for

over

months

A

burst

in

popularity

may

be

self-sustaining

as

popular

URLs

are

displayed

on

the

popular

page

which

users

can

visit

to

learn

what

others

are

currently

talking

about

However

the

initial

cause

of

a

popularity

burst

is

likely

exogenous

to

Delicious

given

that

Delicious

is

a

bookmarking

service

a

mention

on

a

widely

read

weblog

or

website

is

a

plausible

primary

cause

Kumar

et

al.

-LSB-

-RSB-

demonstrate

burstiness

among

links

in

weblogs

and

literature

on

opinion

and

fad

formation

demonstrate

how

well-connected

individuals

and

fashion

leaders

can

spread

information

and

influence

others

-LSB-

-RSB-

Stable

patterns

in

tag

proportions

As

a

URL

receives

more

and

more

bookmarks

the

set

of

tags

used

in

those

bookmarks

as

well

as

the

frequency

of

each

tag

's

use

within

that

set

represents

the

combined

description

of

that

URL

by

many

users

One

might

expect

that

individuals

varying

tag

collections

and

personal

preferences

compounded

by

an

ever-increasing

number

of

users

would

yield

a

chaotic

pattern

of

tags

as

time

goes

on

However

it

turns

out

that

the

combined

tags

of

many

users

bookmarks

give

rise

to

a

stable

pattern

in

which

the

proportions

of

each

tag

are

nearly

fixed

Empirically

we

found

that

usually

after

the

first

or

so

bookmarks

each

tag

's

frequency

is

a

nearly

fixed

proportion

of

the

total

frequency

of

all

tags

used

Figure

and

b

shows

this

pattern

which

was

obtained

by

plotting

the

relative

fractions

of

tags

for

a

given

fixed

URL

-LRB-

vertical

axis

-RRB-

as

a

function

of

time

-LRB-

horizontal

axis

-RRB-

as

measured

in

units

of

bookmarks

added

to

the

same

URL

Each

line

represents

a

tag

as

more

bookmarks

are

added

-LRB-

horizontal

axis

-RRB-

the

proportion

of

the

tags

represented

by

that

tag

-LRB-

vertical

axis

-RRB-

flattens

out

A

web

tool

that

visualizes

Delicious

data

called

Cloudalicious

-LSB-

-RSB-

also

shows

this

pattern

This

stable

pattern

can

be

explained

by

resorting

to

the

dynamics

of

a

stochastic

urn

model

originally

proposed

by

Eggenberger

and

Polya

to

model

disease

contagion

-LSB-

-RSB-

In

its

simplest

form

this

probabilistic

model

consists

of

an

urn

initially

containing

two

balls

one

red

and

one

black

At

each

time

step

a

ball

is

randomly

selected

and

replaced

in

the

urn

along

with

an

additional

ball

of

the

same

color

Thus

after

N

steps

the

urn

contains

N

balls

The

remarkable

property

of

such

a

model

is

that

in

spite

of

its

random

nature

after

a

number

of

draws

a

pattern

emerges

such

that

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

Collaborative

tagging

fraction

of

balls

of

a

given

color

becomes

stable

over

time

Furthermore

that

fraction

converges

to

a

random

limit

This

implies

that

if

the

process

is

run

forever

the

fraction

converges

to

a

limit

but

the

next

time

one

starts

the

process

over

and

runs

it

again

the

stable

fraction

will

converge

to

a

different

number

Notice

that

the

convergence

to

such

a

fixed

limit

is

also

obtained

when

the

initial

distributions

of

balls

of

different

colors

differ

from

each

other

for

many

balls

and

different

replacement

schemes

With

respect

to

tagging

this

means

that

even

if

some

tags

are

initially

more

likely

than

others

one

nevertheless

observes

the

same

kind

of

convergence

to

a

fixed

but

random

limit

This

stability

has

important

implications

for

the

collective

usefulness

of

individual

tagging

behavior

After

a

relatively

small

number

of

bookmarks

a

nascent

consensus

seems

to

form

one

that

is

not

affected

by

the

addition

of

further

tags

Users

may

continually

add

bookmarks

but

the

stability

of

the

overall

system

is

not

significantly

changed

The

commonly

used

tags

which

are

more

general

have

higher

proportions

and

the

varied

personally

oriented

tags

that

users

may

use

can

coexist

with

them

Moreover

because

this

stability

emerges

after

fewer

than

bookmarks

URLs

need

not

become

very

popular

for

the

tag

data

to

be

useful

For

example

Figures

and

show

URL

which

reaches

its

stable

pattern

at

least

days

before

its

spike

in

popularity

Two

reasons

why

this

stabilization

might

occur

are

imitation

and

shared

knowledge

In

the

probabilistic

model

replacement

of

a

ball

with

another

ball

of

the

same

color

can

be

seen

as

a

kind

of

imitation

Likewise

Delicious

users

may

imitate

the

tag

selection

of

other

users

The

Delicious

interface

through

which

users

add

bookmarks

shows

users

the

tags

most

commonly

used

by

others

who

bookmarked

that

URL

already

users

can

easily

select

those

tags

for

use

in

their

own

bookmarks

thus

imitating

the

choices

of

previous

users

This

can

be

helpful

especially

if

a

user

does

not

know

how

to

categorize

a

particular

URL

A

user

may

use

the

suggested

popular

tags

as

a

way

of

looking

to

others

to

see

what

the

right

thing

to

do

is

The

principle

of

social

proof

suggests

that

actions

are

viewed

as

correct

to

the

extent

that

one

sees

others

doing

them

-LSB-

-RSB-

In

this

case

choosing

tags

others

have

already

used

may

seem

like

a

safe

choice

or

one

that

does

not

require

time

or

effort

Imitation

however

does

not

explain

everything

The

interface

of

Delicious

shows

only

a

few

of

the

most

commonly

used

tags

but

the

stable

pattern

persists

even

for

less

common

tags

which

are

not

shown

Shared

knowledge

among

taggers

may

also

account

for

their

making

the

same

choices

Recall

from

above

the

social

aspect

of

sensemaking

We

may

expect

that

users

of

Delicious

or

any

other

tagging

system

share

some

background

linguistic

cultural

educational

and

so

on

In

the

case

of

Delicious

many

users

appear

to

have

strong

technical

backgrounds

as

many

of

the

bookmarked

URLs

are

technology-related

Accordingly

some

documents

may

occupy

roughly

the

same

status

in

many

of

those

users

lives

since

they

may

make

use

of

web

documents

in

the

same

way

users

may

categorize

them

the

same

way

as

well

Part

of

the

reason

these

stable

patterns

emerge

is

that

-LRB-

a

-RRB-

Proportions

of

Tag

Use

-LRB-

b

-RRB-

Proportions

of

Tag

Use

50 100 150 200

250 300 350 400

Fig.

The

stabilization

of

tags

relative

proportions

for

two

popular

URLs

-LRB-

and

-RRB-

The

vertical

axis

denotes

fractions

and

the

horizontal

axis

time

in

units

of

bookmarks

added

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

S.A.

GOLDER

AND

B.A.

HUBERMAN

the

ideas

and

characteristics

that

are

represented

in

tags

are

stable

As

the

ideas

themselves

change

these

stable

states

may

likewise

change

For

example

in

Speroni

-LSB-

-RSB-

one

URL

's

Cloudalicious

chart

shows

surging

growth

of

the

tag

ajax

This

example

illustrates

a

change

in

how

some

users

categorized

the

technologies

they

use

in

their

work

Specifically

the

term

ajax

came

to

represent

a

set

of

technologies

that

previously

had

no

name

but

as

this

concept

came

into

being

it

represented

an

innovation

that

changed

the

way

some

people

thought

and

spoke

about

the

technologies

they

used

as

well

as

the

URLs

they

tagged

about

those

technologies

in

aggregate

as

well

as

individually

in

performing

this

larger

function

across

the

web

Given

the

current

proliferation

of

sites

that

support

collaborative

tagging

we

expect

that

these

sites

will

continue

to

provide

fertile

ground

for

studying

computer-mediated

collaborative

systems

in

addition

to

providing

users

with

new

ways

to

share

and

organize

content

References

-LSB-

-RSB-

J.

Rowley

Organizing

Knowledge

-LRB-

Gower

Brookfield

VT

-RRB-

-LSB-

-RSB-

C.

Shirky

Ontology

is

Overrated

Categories

Links

and

Tags

-LRB-

-RRB-

Available

at

-LRB-

accessed

October

-RRB-

-LSB-

-RSB-

A.

Doan

J.

Madhavan

P.

Domingos

and

A.

Halevy

Learning

to

map

between

ontologies

on

the

semantic

Web

In

Proceedings

of

the

International

World

Wide

Web

Conference

-LRB-

Honolulu

-RRB-

-LRB-

-LRB-

ACM

Press

New

York

-RRB-

-LSB-

-RSB-

A.

Mathes

Folksonomies

Cooperative

Classification

and

Communication

Through

Shared

Metadata

-LRB-

-RRB-

Available

at

-LRB-

accessed

October

-RRB-

-LSB-

-RSB-

Del.icio.us

Available

at

-LRB-

accessed

October

-RRB-

-LSB-

-RSB-

Yahoo

MyWeb

Available

at

-LRB-

accessed

October

-RRB-

-LSB-

-RSB-

CiteULike

Available

at

-LRB-

accessed

October

-RRB-

-LSB-

-RSB-

Connotea

Available

at

-LRB-

accessed

October

-RRB-

-LSB-

-RSB-

Technorati

Available

at

-LRB-

accessed

October

-RRB-

-LSB-

-RSB-

W.

Jones

A.

Phuwanartnurak

R.

Gill

and

H.

Bruce

Do

n't

take

my

folders

away

Organizing

personal

information

to

get

things

done

In

Proceedings

of

the

ACM

Conference

on

Human

Factors

in

Computing

Systems

-LRB-

CHI

-RRB-

April

Portland

OR

-LRB-

ACM

Press

New

York

-RRB-

-LSB-

-RSB-

J.

Pustejovsky

The

Generative

Lexicon

-LRB-

MIT

Press

Cambridge

MA

-RRB-

-LSB-

-RSB-

J.

Tanaka

and

M.

Taylor

Object

categories

and

expertise

is

the

basic

level

in

the

eye

of

the

beholder

Cognitive

Psychology

-LRB-

-RRB-

-LRB-

-RRB-

-LSB-

-RSB-

K.

Weick

K.

Sutcliffe

and

D.

Obstfeld

Organizing

and

the

process

of

sensemaking

Organizational

Science

-LRB-

-RRB-

-LRB-

-RRB-

-LSB-

-RSB-

W.

Labov

The

boundaries

of

words

and

their

meanings

In

C.J.

Bailey

and

R.

Shy

-LRB-

eds

-RRB-

New

Ways

of

Analyzing

Variation

in

English

-LRB-

-RRB-

-LSB-

-RSB-

S.

Dumais

E.

Cutrell

J.

Cadiz

G.

Jancke

R.

Sarin

and

Conclusion

We

have

observed

that

collaborative

tagging

users

exhibit

a

great

variety

in

their

sets

of

tags

some

users

have

many

tags

and

others

have

few

Tags

themselves

vary

in

frequency

of

use

as

well

as

in

what

they

describe

Nevertheless

because

stable

patterns

emerge

in

tag

proportions

minority

opinions

can

coexist

alongside

extremely

popular

ones

without

disrupting

the

nearly

stable

consensus

choices

made

by

many

users

While

this

study

was

focused

on

Delicious

we

expect

that

these

findings

will

apply

to

other

similar

tagging

systems

The

prevalence

of

tagging

with

a

very

large

number

of

tags

and

according

to

information

intrinsic

to

the

tagger

demonstrates

that

a

significant

amount

of

tagging

if

not

all

is

done

for

personal

use

rather

than

public

benefit

Nevertheless

even

information

tagged

for

personal

use

can

benefit

other

users

For

example

if

many

users

find

something

funny

there

is

a

reasonable

likelihood

someone

else

would

also

find

it

to

be

so

and

may

want

to

explore

it

Likewise

one

might

want

to

read

something

that

many

other

people

have

decided

they

want

toread

as

well

In

this

way

Delicious

functions

as

a

recommendation

system

even

without

explicitly

providing

recommendations

However

information

tagged

by

others

is

only

useful

to

the

extent

that

the

users

in

question

make

sense

of

the

content

in

the

same

way

so

as

to

overlap

in

their

classification

choices

The

stable

consensus

choices

that

emerge

may

be

used

on

a

large

scale

to

describe

and

organize

how

web

documents

interact

with

one

another

Currently

this

is

being

performed

problematically

on

a

small

scale

by

experts

and

equally

problematically

on

a

large

scale

by

machines

-LSB-

-RSB-

The

stability

we

have

shown

here

demonstrates

that

tagged

bookmarks

may

be

valuable

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

Collaborative

tagging

-LSB-

-RSB-

-LSB-

-RSB-

-LSB-

-RSB-

-LSB-

-RSB-

D.

Robbins

Stuff

I

've

seen

a

system

for

personal

information

retrieval

and

re-use

In

Proceedings

of

the

ACM

Conference

on

Information

Retrieval

July

to

August

Toronto

-LRB-

ACM

Press

New

York

-RRB-

T.

Coates

Two

Cultures

of

Fauxonomies

Collide

-LRB-

-RRB-

Available

at

-LRB-

accessed

October

-RRB-

M.

Biddulph

Introducing

Del.icio.us.XML.com

-LRB-

-RRB-

Available

at

html

-LRB-

accessed

October

-RRB-

R.

Kumar

J.

Novak

P.

Raghavan

and

A.

Tomkins

On

the

bursty

evolution

of

blogspace

In

Proceedings

of

The

International

World

Wide

Web

Conference

May

Budapest

F.

Wu

and

B.

Huberman

Social

Structure

and

Opinion

Formation

-LRB-

-RRB-

Available

at

-LSB-

-RSB-

-LSB-

-RSB-

-LSB-

-RSB-

-LSB-

-RSB-

-LSB-

-RSB-

-LRB-

accessed

October

-RRB-

S.

Bikhchandani

D.

Hirshleifer

and

I.

Welch

Learning

from

the

behavior

of

others

conformity

fads

and

informational

cascades

Journal

of

Economic

Perspectives

-LRB-

-RRB-

-LRB-

-RRB-

Cloudalicious

Available

at

-LRB-

accessed

October

-RRB-

F.

Eggenberger

and

G.

Polya

ber

die

Statistik

verkettete

Vorg

nge

Zeitschrift

f

r

Angewandte

Mathematik

und

Mechanik

-LRB-

-RRB-

R.

Cialdini

Influence

Science

and

Practice

-LRB-

Allyn

and

Bacon

Boston

MA

-RRB-

P.

Speroni

Tagclouds

and

Cultural

Changes

-LRB-

-RRB-

Available

at

-LRB-

accessed

October

-RRB-

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

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