STUDY the Social Web through Analysis and Network Science

Social Web 
2016
Lecture 6: How can we STUDY the Social Web?
(based on slides from Les Carr, Nigel Shadbolt, Harith Alani)
Davide Ceolin (credits to: Lora Aroyo)
The Network Institute
VU University Amsterdam

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 1010 people
more than 1011 web documents
Social Web 2016, Davide Ceolin

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 Observatory

http://websci15.org/accepted-submissions

Web is NOT a Thing
• it’s not a verb, nor 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

Slide from Harith Alani Social Web 2016, Davide Ceolin

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: # rows / object / bytes
• Variety: # 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

Web Science Reﬂections
Is the Web changing faster than our ability to observe it?
How to measure or instrument the Web?

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

User State is Important
• the original Web graph is too simple, starts from quasi static HTML
• for personalization or customization different representations (of
sources) may be served to different requesters, e.g. cookies
• graph-based models often do not account for this sort of user-
dependent state, and not ﬁt for all the information behind the
servers, in DeepWeb
• it’s not a simple HTTP-GET anymore (but HTTP-POST or HTTP-
GET with complex URI) that is the basis for deﬁning nodes in the
graph
• URis that carry user state are heavily used in Web applications, but
are not in the model and largely unanalyzed

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

Wikipedia
• purely mathematical (technology-based) models do not capture the
whole story
• the Wikipedia structure (link labels) shows a Zipf-like distribution
just like other tag-based systems
• Wikipedia is built on MediaWiki software
• but other MediaWiki-based applications did not generate such
signiﬁcant use
• the pure 'technological' explanation cannot explain it
• must be related to the 'social model' of how Wikipedia is
organized
this is referred to as the dynamics of a 'social machine' (already inTBL’s original vision ofWWW)
Social Web 2014, Lora Aroyo!

Collective Intelligence
• why do people contribute?
• how to maintain the connected content?
• how are trust & provenance represented, maintained
and repaired on the Web?
Social Web 2014, Lora Aroyo!Social Web 2016, Davide Ceolin

Collective Intelligence
Motivation Example Mean
Fun “Writing inWikipedia is fun” 6,1
Ideology “I think information should be free” 5,59
Values “I feel it’s important to help others” 3,96
Understanding “Writing inWikipedia allows me to gain a new perspective on things” 3,92
Enhancement “Writing inWikipedia makes me feel needed” 2,97
Protective “By writing inWikipedia I feel less lonely” 1,97
Career “I can make new contacts that might help my career” 1,67
Social “People I am close to want me to write inWikipedia” 1,51

Social Machines
• today's interactive applications are very early
social machines limited by being largely isolated from
one another
• more effective social machines can be expected
• social processes in society interlink, so they
should also interlink on the web
• technology needed to allow user communities to
construct, share & adapt social machines to get
success through trial, use & reﬁnement
Social Web 2014, Lora Aroyo!Social Web 2016, Davide Ceolin

it’s relationships, stupid!
not attributes
May, 2007April, 2002
All the world's a net
by David Cohen

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

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)

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

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

Web Science Reﬂections
How to identify behaviors and patterns?
How to analyze the changing structure of the Web?

The Age of OPEN Data

The Age of OPEN Data
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

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

Hands-on Teaser
• Present your social web app pitch
• 12 March (10:00 - 12:45)
• C.623 all groups together
• 1 mins presentation time
• be on time
• send your slide(s) the day before via the website

STUDY the Social Web through Analysis and Network Science

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