Axa Assurance Maroc - Insurer Innovation Award 2024
Lecture 7: How to STUDY the Social Web? (2014)
1. 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
2. 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
Social Web 2014, Lora Aroyo!
3. The Web
Great success as a technology,
it’s built on significant computing infrastructure,
but
as an entity surprisingly unstudied
Social Web 2014, Lora Aroyo!
4. • 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
Social Web 2014, Lora Aroyo!
Science & Engineering
5. 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
Social Web 2014, Lora Aroyo!
7. eScience: Analysis of Data
Social Web 2014, Lora Aroyo!
• 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?
8. Simple micro rules give rise to
complex macro phenomena
Social Web 2014, Lora Aroyo!
• 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
9. • 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
Social Web 2014, Lora Aroyo!
A new way of software
development
10. 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
Social Web 2014, Lora Aroyo!
11. 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
Social Web 2014, Lora Aroyo!
12. 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
Social Web 2014, Lora Aroyo!
13. 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?
Social Web 2014, Lora Aroyo!
14. 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
Social Web 2014, Lora Aroyo!
16. it’s relationships, stupid!
not attributes
May, 2007
April, 2002
All the world's a net
by David Cohen
Social Web 2014, Lora Aroyo!
17. 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
Social Web 2014, Lora Aroyo!
18. 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)
Social Web 2014, Lora Aroyo!
19. 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
Social Web 2014, Lora Aroyo!
21. 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
Social Web 2014, Lora Aroyo!
24. Web Science is about
additionality
not the union of
disciplines, but
intersection
Social Web 2014, Lora Aroyo!
25. Society is Diverse
different parts of society have different objectives and hence incompatible
Web requirements, e.g. openness, security, transparency, privacy
Social Web 2014, Lora Aroyo!
26. • 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.
Social Web 2014, Lora Aroyo!
Understanding the
Socio-Cultural
27. Understanding variations
Social Web 2014, Lora Aroyo!
• 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
28. Social Web 2014, Lora Aroyo!
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
29. Social Web 2014, Lora Aroyo!
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
30. Social Web 2014, Lora Aroyo!
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
31. but
How to do the Science?
Social Web 2014, Lora Aroyo!
32. 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
Social Web 2014, Lora Aroyo!
35. Social Web 2014, Lora Aroyo!
The Age of OPEN Data
TRANSPARENCY VALUE ENGAGEMENT
36. Social Web 2014, Lora Aroyo!
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
44. 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?
Social Web 2014, Lora Aroyo!
45. 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?
Social Web 2014, Lora Aroyo!
46. 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?
Social Web 2014, Lora Aroyo!
47. Social Web 2014, Lora Aroyo!
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?
48. What can you do as a
Computer Scientist?
specifically for the SocialWeb
Social Web 2014, Lora Aroyo!