Lecture 6: How can we STUDY the (Social) Web? (VU Amsterdam Social Web Course)

Lecture VI: How can we STUDY the Social Web?(based on slides from Les Carr, Nigel Shadbolt, Harith Alani

Davide Ceolin and Lora Aroyo The Network Institute

VU University Amsterdam

Social Web 2015

Social Web 2015, Davide Ceolin and Lora Aroyo

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 10more than 10


The Web

Great success as a technology,it’s built on significant computing infrastructure,

butas 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

Social Web 2014, Lora Aroyo!

slides from: david de roure

How to make such questions

part of Web Science

problems?

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 2015, Davide Ceolin and Lora Aroyo

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


Is a single unified view the goal? Is

it achievable?

macro vs. micro level predictions

• 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 documents2: people were gaming to influence algorithms &

improve their search rank3: 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


According to Googleeach 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 serversincreasing diversity in users



it’s relationships, stupid! not attributes

May, 2007April, 2002

All the world's a net by David Cohen


http://www.newscientist.com/search?rbauthors=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


http://www.google.nl/search?tbo=p&tbm=bks&q=inauthor:%22Albert-L%C3%A1szl%C3%B3+Barab%C3%A1si%22

NYT, 26 Feb 2007

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)


http://en.wikipedia.org/wiki/Social_network

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


http://webscience.ecs.soton.ac.uk/

Web Science is about additionality

not the union of disciplines, but intersection


Society is Diversedifferent 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 the Web - 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


butHow to do the Science?


Big Data OwnersWho can do macro analysis?

• Google, Bing, Yahoo!, Baidu

• Large scale, comprehensive data

• New forms of research alliance

How Billions of Trivial 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 Social Web


Hands-on Teaser

• Present your social web app pitch • 12 March (11:00 - 12:45)

• C.623 all groups together • 1 mins presentation time • be on time • send your slide(s) the day before via the website


Education

Lecture 6: How can we STUDY the (Social) Web? (VU Amsterdam Social Web Course)