Lecture 7: How to STUDY the Social Web? (2014)

Social Web 2014, Lora Aroyo!
Lecture VI: How can we STUDY the Social Web?
(based on slides from Les Carr, Nigel Shadbolt, Harith Alani
Lora Aroyo
The Network Institute

VU University Amsterdam
Social Web
2014

The Web
the most used and one of the most transformative applications
in the history of computing, e.g. how the Social Web has
transformed the world's communication
!
approximately 10
more than 10

The Web
Great success as a technology,

it’s built on signiﬁcant computing infrastructure,

but

as an entity surprisingly unstudied

• physical science: analytic discipline to ﬁnd laws that
generate or explain observed phenomena

• CS is mainly synthetic: formalisms & algorithms are
created to support speciﬁc desired behaviors

• Web Science: web needs to be studied & understood
as a phenomenon but also to be engineered for future
growth and capabilities
Science & Engineering

Web is NOT a Thing
• it’s not a verb, or a noun

• it’s a performance, not
an object

• co-constructed with
society

• activity of individuals
who create interlinked
content that reﬂect &
reinforce the
interlinkedness of
society & social
interaction
... and a record of
that performance

eScience: Analysis of Data
• the automated or semi-automated extraction of
knowledge from massive volumes of data — it is a
lot, but it is not just a matter of volume
• 3 Vs of Big Data
• Volume: #of rows / object / bytes

• Variety: # of columns / dimensions / sources

• Velocity: # columns / bytes per unit time

• more Vs — Veracity: Can we trust this data?

Simple micro rules give rise to
complex macro phenomena
• at microscale an infrastructure of artiﬁcial languages and protocols:
a piece of engineering

• however, interaction of people creating, linking and consuming
information generates web's behavior as emergent properties at
macroscale

• properties require new analytic methods to be understood

• some properties are desirable and are to be engineered in, others
are undesirable and if possible engineered out

• software applications designed based on appropriate
technology (algorithm, design) and with envisioned
'social' construct

• usually tested in the small, testing microscale properties

• a macrosystem evolving from people using the
microsystem and interacting in often unpredicted ways, is
far more interesting and must be analyzed in different
ways

• macrosystems exhibit challenges that do not exist at
microscale
A new way of software
development

Example:
Evolution of Search Engines
1: techniques designed to rank documents

2: people were gaming to inﬂuence algorithms &
improve their search rank

3: adapt search technologies to defeat this inﬂuence

The Web Graph
• to understand the web, in good CS
tradition, we look at the graph

• nodes are web pages (HTML)

• edges are hypertext links
between nodes

• ﬁrst analysis shows that in-degree
and out-degree follow power law
distribution => holds for large
samples

• this gave insight into the growth of
the web

The (Search) Algorithms
• the Web graph also as basis of
algorithms for search engines:

• PageRank and others
assume that inserting a
hyperlink symbolizes an
endorsement of authority of
the page linked to

According to Google
each day 20-25% of searches have not been seen before, i.e.

generate a new identiﬁer

thus a new node in the graph

more than 20 million new links per day, 200 per second

!
do they follow the same power laws & growth models?

According to Google
each day 20-25% of searches have not been seen before, i.e.

generate a new identiﬁer

thus a new node in the graph

more than 20 million new links per day, 200 per second

!
do they follow the same power laws & growth models?
validating such models is hard
exponential growth of content

changes in number & power of servers

increasing diversity in users

it’s relationships, stupid!
not attributes
May, 2007

April, 2002

All the world's a net
by David Cohen

Leveraging recent advances in:

• Theories: about social motivations for creating, maintaining, dissolving & re-creating
links in multidimensional networks & about emergence of macro-structures

• Data: Semantic Web provides technological capability to capture, store, merge &
query relational metadata to more effectively understand & enable communities

• Methods: qualitative & quantitative for theoretically-grounded network predictions

• Computational infrastructure: Cloud computing & petascale applications are
critical to face the computational challenges in analyzing the data

Network
Analysis
• is about linking social actors, e.g.
systematically understanding
and identifying connections

• by using empirical data

• draws on graphic imagery

• relies on mathematical/
computational models

• Jacob Moreno - one of the
founders of social network
analysis; some of the earliest
graphical depictions of social
networks (1933)

Think Networks!
• everything is connected to everything else

• networks are pervasive - from the human brain
to the Internet to the economy to our group of
friends

• following underlying order and follow simple laws

• "new cartographers" are mapping networks in a
wide range of scientiﬁc disciplines

• social networks, corporations, and cells are more
similar than they are different

• new insights into the interconnected world

• new insights on robustness of the Internet, spread
of fads and viruses, even the future of democracy.
Albert-László Barabási: Linked:The New Science of Networks

April, 2002


Networks:
another perspective :-)
• Social Networks: It’s not what you know,
it’s who you know

• Cognitive Social Networks: It’s not who
you know, it’s who they think you know.

• Knowledge Networks: It’s not what you
know, it’s what they think you know

http://webscience.ecs.soton.ac.uk/ L.A. Carr, C.J. Pope,W. Hall,N.R. Shadbolt

Web Science is about
additionality
not the union of
disciplines, but
intersection

Society is Diverse
different parts of society have different objectives and hence incompatible
Web requirements, e.g. openness, security, transparency, privacy

• POWER DISTANCE:The extent to which power
is distributed equally within a society and the
degree that society accepts this distribution.

• UNCERTAINTY AVOIDANCE:The degree to
which individuals require set boundaries and
clear structures

• INDIVIDUALISM vs COLLECTIVISM:The degree
to which individuals base their actions on self-
interest versus the interests of the group.

• MASCULINITY vs FEMININITY:A measure of a
society's goal orientation

• TIME ORIENTATION:The degree to which a
society does or does not value long-term
commitments and respect for tradition.
Understanding the
Socio-Cultural

Understanding variations
• Ecology of theWeb - structure of
the environment, producers
and consumers

• Populations (individuals and
species), traits/characteristics,
heredity, genotypes and
phenotypes

• Mechanisms - variation
(mutation, migration, genetic
drift), selection

• Outcomes - adaption, co-
evolution, competition, co-
operation, speciation, extinction

but
How to do the Science?

Big Data Owners
Who can do macro analysis?

• Google, Bing,Yahoo!, Baidu

• Large scale, comprehensive data

• New forms of research alliance

!
!
How Billions ofTrivial Data Points can Lead to
Understanding

The Age of OPEN Data

TRANSPARENCY VALUE ENGAGEMENT

TRANSPARENCY VALUE ENGAGEMENT
• common standards for release of public data

• common terms for data where necessary

• licenses - CC variants

• exploitation & publication of distributed, decentralised information assets

Web Observatory

Web Science Reﬂections
Is the Web changing faster than our ability to observe it?
How to measure or instrument the Web?
How to identify behaviors and patterns?
How to analyze the changing structure of the Web?

Big Bang:
Web Information
• the assumption of open exchange of information is
being imposed on the society

• is the Web, and its open access, open data, scientiﬁc &
creative commons offer a beneﬁcial opportunity or
dangerous cul-de-sac?

Open Questions
• How is the world changing as other parts of society impose their
requirements on the Web?, e.g. current examples with SOTA/PIPA,ACTA
requirements for security and policing taking over free exchange of information,
unrestricted transfer of knowledge

• Are the public and open aspects of the Web a fundamental change in
society’s information processes, or just a temporary glitch?, e.g. are open
source, open access, open science & creative commons efﬁcient alternatives to
free-based knowledge transfer?

Open Questions
• do we take Web for granted as provider of a free & unrestricted
information exchange?

• is Web Science the response to the pressure for the Web to change - to
respond to the issues of security, commerce, criminality & privacy?

• what is the challenge for Web science in explaining how the Web impacts
society?

What can you do as a
Computer Scientist?
speciﬁcally for the SocialWeb

image source: http://www.ﬂickr.com/photos/bionicteaching/1375254387/Social Web 2014, Lora Aroyo!
Hands-on Teaser
• Present your ﬁnal assignment (social web app)

Lecture 7: How to STUDY the Social Web? (2014)

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Lecture 7: How to STUDY the Social Web? (2014)