Social computation of emergent networks on user generated content

Knowledge Management Institute

Social Computation of Emergent
Networks on User-Generated Content

GI Workshop on “Web-Science” at
Informatik 2010 der 40. Jahrestagung der Gesellschaft für Informatik
2010, 40
Leipzig, Germany

Markus Strohmaier
Assistant Professor

g g
Graz University of Technology, Austria
e-mail: markus.strohmaier@tugraz.at
web: http://www.kmi.tugraz.at/staff/markus

Markus Strohmaier 2010
1


Social-Computational Systems
… is the title of a new National Science Foundation (NSF) Program.
( ) g

the genesis of a new class of computational systems,
which generate emergent behaviors that arise out of the complex and
dynamic interactions among people and computers.
Source: National Science Foundation http://www.nsf.gov/pubs/2010/nsf10600/nsf10600.htm
p g p

3 observations:
• Rise of User Generated Content
• 5 out of the top 10 websites in the world have a focus on user-generated-content
(Alexa.com 2010)
• Rise of Online Social Networks
– More than 500 million active Facebook users, 50% log on any given day (Facebook 2010)
• Integration of user data and system functionality
• User data becomes an integral part of system functions

(Facebook 2010) https://www.facebook.com/press/info.php?statistics 2


Social Computational Systems

Interaction between individuals and
computational systems
is mediated by the aggregate behavior of
y gg g
users.

3


Social Computation
p
influences system properties (X)

X=Findability X=Utility

It is through the process of social computation, i.e.
the combination of social behavior and algorithmic computation,
that system properties and functions emerge.

X=Navigability
X Navigability X=Relevance
X R l

4


System Properties of

• Findability:
• the ease at which a document can be found by a user

• Utility:
U ili
• the degree to which a system maximizes usefulness of its functions for users

• Navigability:
• the
th ease at which a user can navigate f
t hi h i t from A t B
to

• Relevance:
• the extent to which offered information is considered relevant

• Privacy:
• the extent to which private information is kept private

• Profit:
• The extent to which functions can be monetized

• …
influenced by social computation processes

5


Agenda
1. Social-Computational S t
1 S i lC t ti l Systems

2. Navigability of Social-Computational Systems

3. Semantics in Social-Computational Systems

4. Social-Computational Systems & the Future

6


Agenda




7


Example:
X = Connectivity (of the web graph)

Questions:
• What is X like? • What causes X?
bow-tie architecture
of the web

[Broder et al 2000]
8


Example:
X = Connectivity (of the web graph)

Questions:
• What is X like? • What causes X? • How can we
bow-tie architecture Social mechanisms, such as improve X?
of the web preferential attachment

an open issue
p

[Broder et al 2000] [Barabasi 1999]
9


Social Computational Systems:
What type of questions are we asking?
e.g. X = Connectivity of the web graph
C ti it f th b h
• Description and Classification: • Causality:
• What is X like? • Does X cause Y?
• What are its properties? • Does X prevent Y?
• How can it be categorized? • What causes X?
• How can we measure it? • What effect does X have on Y?
• Descriptive Process: • Causality - Comparative:
• How does X work? • Does X cause more Y than does Z?
• What is the process by which X • Is X better at preventing Y than is Z?
pp
happens? • Does X cause more Y than does Z
• How does X evolve? under one condition but not others?
• Descriptive Comparative: • Design
• How does X differ from Y? • What is an effective way to achieve X?
y
• Relationship: • How can we improve X?
• Are X and Y related?
• Do occurences of X correlate with
occurences of Y?
cf. [Easterbrook 2007 et al.]
Selecting Empirical Methods for Software Engineering Research, Steve Easterbrook, Janice Singer, Margaret-Anne Storey, Daniela Damian, "Selecting Empirical Methods for Software 10
Engineering Research", Guide to Advanced Empirical Software Engineering, 2007


Attempting a Definition:
…refer to systems in which essential system properties and
functions (“X”) are influenced by the behavior of users.

Thus, certain system properties and functions are not engineered
by a single person, but they are emergent, i.e. the result of
aggregating information from a large group of usersusers.

In this sense, certain system properties and functions of social-
computational systems are b
i l beyond the direct control of system
d h di l f
designers.

New approaches for designing and shaping
social-computational systems are needed.

11


The Dual Nature of Web-Science

Science Engineering

What is X like?
Improve X? Prevent Y?
typically
beyond
control

social computation =
social behavior + algorithmic computation
emergent social-computational
system properties and f
functions

through aggregation

12


Social Computational Systems:
What type of questions are we asking?
• Description and Classification: • Causality:
• What is X like? • Does X cause Y?
• What are its properties? • Does X prevent Y?
• How can it be categorized? • What causes X?
• How can we measure it? • What effect does X have on Y?
• Descriptive Process: • Causality - Comparative:
• How does X work? • Today‘s talk: Y than does Z?
Does X cause more
• What is the process by which X • X1=Navigability
Is X better at preventing Y than is Z?
pp
happens? • X2=Semantics Y than does Z
Semantics
Does X cause more
• How does X evolve? of User-Generated not others?
under one condition but Content
• Descriptive Comparative:
• How does X differ from Y? • Design
• Relationship: • What is an effective way to achieve X?
• Are X and Y related? • How can we improve X?
• Do occurences of X correlate with
occurences of Y?
cf. [Easterbrook 2007 et al.]
Selecting Empirical Methods for Software Engineering Research, Steve Easterbrook, Janice Singer, Margaret-Anne Storey, Daniela Damian, "Selecting Empirical Methods for Software 14
Engineering Research", Guide to Advanced Empirical Software Engineering, 2007


Agenda




15


X1=Navigability
g y

Question:
How can we Measure and Improve
Navigability in Social Tagging S t
N i bilit i S i l T i Systems?
?

Tag clouds as an instrument for
g
navigation

16


Tag Clouds are Supposed to be Efficient
Tools for Navigating Tagging Systems
The Navigability Assumption:
• An implicit assumption among designers of social tagging
systems that tag clouds are specifically useful to
support navigation.
• This has hardly been tested or critically reflected in the past
past.

Navigating tagging systems via tag clouds:
1) The system presents a tag cloud to the user.
) y p g
2) The user selects a tag from the tag cloud.
3) The system presents a list of resources tagged with the
selected tag
tag.
4) The user selects a resource from the list of resources.
5) The system transfers the user to the selected resource,
and th process potentially starts anew.
d the t ti ll t t
17


Navigability of Social Tagging Systems
Question: How does
(i) th size of t clouds and
the i f tag l d d
(ii) number of resources / tag
influence the navigability (X1) of social tagging systems?

established
systems,
many users

New system,
few users

18


Defining Navigability

A network is navigable iff:
There is a path between all or almost all pairs of nodes
in the t
i th network. k

Formally:
1. There exists a giant component
2.
2 The effective diameter is low (bounded by log n)

J. Kleinberg. The small-world phenomenon: An algorithmic perspective. Proc. 32nd ACM Symposium on Theory of Computing, 2000. Also appears as Cornell Computer Science
Technical Report 99-1776 (October 1999)
19


Navigability: Examples

Example 1:

Not navigable: No giant component

Example 2:

Not navigable: giant component BUT
component,
avg. shortest path > log2(9)

20


Navigability: Examples

Example 3:

Navigable: Giant component AND
avg.
avg shortest path ≤ 2 < log2(9)

Is this efficiently navigable?
There are short paths between all nodes, but can an
agent or algorithm find them with local knowledge
only?
21


Efficiently navigable

A network is efficiently navigable iff:
If there is an algorithm that can find a short path with
only l
l local k
l knowledge ( ith b
l d (with branching f t k) and
hi factor k), d
the delivery time of the algorithm is bounded
polynomially by logk(n).
B
Example 4:
p

A C

Efficiently navigable, if the algorithm knows it needs to
go through A B C

J. Kleinberg. The small-world phenomenon: An algorithmic perspective. Proc. 32nd ACM Symposium on Theory of Computing, 2000. Also appears as Cornell Computer Science
22
Technical Report 99-1776 (October 1999)


User Interface constraints

Tag Cloud Size n
n: number of tags
shown per tag cloud

(topN most common algorithm)

Pagination of resources / tag
k: number of resources
shown per page

(reverse chronological ordering)

23


How UI constraints effect Navigability
Tag Cloud Size

Pagination

Limiting the tag cloud size n to practically feasible sizes (e.g. 5, 10, or more) does
not influence navigability (this is not very surprising).
BUT: Limiting the out-degree of high frequency tags k (e.g. through pagination
with resources sorted in reverse-chronological order) leaves the network
vulnerable to fragmentation. This destroys navigability of prevalent approaches
to tag clouds.
24


Findings
1. For
1 F certain specific, b t popular, t cloud scenarios, th
t i ifi but l tag l d i the
so-called Navigability Assumption does not hold.
2. While we could confirm that tag-resource networks have
g
efficient navigational properties in theory, we found that
popular user interface decisions significantly impair
navigability.
navigability

These results make a theoretical and an empirical argument
against existing approaches to tag cloud construction.

How can we improve the navigability of social tagging
systems?

25


Recovering Navigability in Social Tagging
Systems
Instead of reverse-chronological ordering of resources,
we apply a random ordering.

26


Efficient Navigability in Social Tagging
Systems
Instead of random ordering, we use hierarchical
background knowledge for ranking paginated
resources [Kleinberg 2001].

J. M. Kleinberg, “Small-world phenomena and the dynamics of information,” in Advances in Neural Information Processing Systems (NIPS), 14. MIT Press,
27
2001, p. 2001.


Implications

• Navigability in social tagging systems is an emergent
system property

• S
Some of our initial intuitions about navigability (t
f i iti l i t iti b t i bilit (tag
clouds) are wrong

• The UI represents an opportunity to influence
emergent system properties

28


Agenda




29


X1=Semantics

Question:
How can we Measure and Influence
Emergent Semantics in Social Tagging
Systems?
S t ?

30


Emergent Semantic Structures

Markus Strohmaier 2010 Lerman et al 2010
31


Pragmatics influence emergent properties
Motivations for Tagging
M ti ti f T i Kinds f T
Ki d of Tags

• Future Retrieval • Content-based
• Contribution and Sharing • Context-based
• Attracting Attention (Flickr) • Attribute Tags
• Play and Competition (ESP
This suggests that … • Ownership Tags
Game) emergent semantics are influenced by the Tags
• Subjective
• underlying motivation for tagging
Self Presentation
(cf. f
( f for example, [Heckner 2009])
l [H k • Organizational Tags
• Opinion Expression • Purpose Tags
• Task Organization (“toread”) • Factual Tags
• ( for:scott )
Social Signalling (“for:scott”) • P
Personal T
l Tags
• Money (Amazon Mechanical • Self-referential tags
Turk) • Tag Bundles
g
• Categorization / Description
Gupta et al. 2010 32


Why Do Users Tag?
One ( f
O (of many) answers:
)
To categorize or to describe resources
Categorizer (C) Describer (D)

Goal later browsing later search
Change of vocabulary costly cheap
Size f
Si of vocabulary
b l limited
li it d Open
O
Tags subjective objective

Example tag clouds

Semantic Assumption:
Categorizers produce more precise emergent semantics than Describers.

M. Strohmaier, C. Koerner, R. Kern, Why do Users Tag? Detecting Users' Motivation for Tagging in Social Tagging Systems, 4th International AAAI Conference on Weblogs and Social Media
33
(ICWSM2010), Washington, DC, USA, May 23-26, 2010.


Measures for
Tagging Pragmatics vs. Tag Semantics
Categorizer/Describer:
C t i /D ib Semantics: [Cattuto et al 2008]
S ti
• Size of tag vocabulary • Co-occurrence count

• Tags per resource • Cosine similarity (TagCont)

• Tags per post • FolkRank
[Hotho et al 2006]
• Orphaned tags

C. Körner, D. Benz, A. Hotho, M. Strohmaier, G. Stumme, Stop Thinking, Start Tagging: Tag Semantics Emerge From Collaborative Verbosity, 19th International World Wide Web Conference
(WWW2010), Raleigh, NC, USA, April 26-30, ACM, 2010.
34


Experimental Setup
As dataset, we used
A ad t t d
• a crawl from Delicious (University of Kassel)
• from November 2006 (containing 667,128 users)
• 10.000 most common tags, minimum of 100 resources / user

For semantic grounding, we used
• WordNet as a knowledge base (cf. [Cattuto et al. 2008])
• Jiang-Conrath as a measure of similarity
• combines the taxonomic path length between to nodes in WordNet with an information-
theoretic similarity measure [Jiang and Conrath 1997]

• A WordNet library as an implementation
• by [Pedersen et al 2004]

35


Results
Describers outperform categorizers on precision of
emergent tag semantics
Categorizers perform Describers perform
worse than random better than random

worse Random Random
users users

better

Categorizers Describers

Markus Strohmaier
2010
36


Implications

• Semantics in social tagging systems is an emergent
system property

• S
Some of our initial i t iti
f i iti l intuitions about semantics are
b t ti
wrong
• describers outperform categorizers on a particular task

• User behavior influences emergent system properties

37


Agenda




38


Social-Computational Systems:
Conclusions
1. Certain properties of social computational systems (such as
navigability or semantics) are emergent p p
g y ) g properties, they are
, y
beyond the direct influence of system designers
2. The user interface is an opportunity to influence these emergent
properties
3. If user motivation or behavior changes over time, system
properties may change.

It is through the process of social computation, i.e.
the combination of social behavior and algorithmic computation,
that system properties and functions emerge.

39


Web-Science: A Call to Action

As web scientists, we need to
• study and map the complex relationships between user behavior
behavior,
user interfaces and emergent properties
• understand the potentials and limits of influencing emergent
system properties
t ti

As web engineers, we need to
• shift perspective away from designing towards shaping social-
computational systems
• reconcile user behaviors with desired system properties

40


End of Presentation

Thank you!

Markus Strohmaier
Graz University of Technology, Austria
y gy,

in collaboration with:
H.P. Grahsl, D. Helic, C. Körner, R. Kern, C. Trattner,
D. Benz, A. Hotho, G. Stumme

42


Related Publications

• Intent and motivation in social media
M. Strohmaier, C. Koerner, R. Kern, Why do Users Tag? Detecting Users' Motivation for Tagging in Social
Users
Tagging Systems, 4th International AAAI Conference on Weblogs and Social Media (ICWSM2010), Washington,
DC, USA, May 23-26, 2010.

• Social computation and emergent structures
C. Körner, D. Benz, A. Hotho, M. Strohmaier, G. Stumme, Stop Thinking, Start Tagging: Tag Semantics Arise
From Collaborative Verbosity, 19th International World Wide Web Conference (WWW2010), Raleigh, NC, USA,
April 26-30, ACM, 2010.
26 30,
D. Helic, C. Trattner, M. Strohmaier and K. Andrews, On the Navigability of Social Tagging Systems, The 2nd
IEEE International Conference on Social Computing (SocialCom 2010), Minneapolis, Minnesota, USA, 2010.

• Knowledge acquisition from social media
C. Wagner, M. Strohmaier, The Wisdom in Tweetonomies: Acquiring Latent Conceptual Structures from
Social Awareness Streams, Semantic Search 2010 Workshop (SemSearch2010), in conjunction with the 19th
International World Wide Web Conference (WWW2010), Raleigh, NC, USA, April 26-30, ACM, 2010.

43

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