Wwsss intro2016-final

Steffen Staab Web Science 1Institute for Web Science and Technologies · University of Koblenz-Landau, Germany
Web and Internet Science Group · ECS · University of Southampton, UK &
Introduction to Web Science
Steffen Staab
University of Southampton &
Universität Koblenz-Landau

Steffen Staab Web Science 2
Welcome in Koblenz!

9.00 am 10.30 am 3.00 pm
Thu Introduction to Web
Science
Noshir Contractor Tutorial Poster session
Fri Internet and Law Nikolaus Forgo Tutorial Project work
Sat Web and Politics Stéphane Bazan Tutorial Project work
Mon Social Machines Jim Hendler Tutorial Project work
Tue Web Entrepreneurship Simon Köhl Tutorial Project work
Wed Computational Social
Science
Nuria Oliver Tutorial Project presentation
Program Overview
Details at http://wwsss16.webscience.org/schedule

• Work independently and self-organised
• Get feedback from your advisor
• Choose your working space:
– D 238, D 239, A 308, B 005 on weekdays
– E 412, E 413, E 414, A 308, B 005 on Saturday
– or anywhere you like
• Final presentation on Wednesday afternoon
(10 minutes per team)
Project work
Details at http://wwsss16.webscience.org/schedule

Social Events
Thu Cocktails at city beach 6.00 pm Meet at main campus entry
Fri SommerUni party 8.00 pm Campus
Sat Free time Old town, KaleidosKOp festival,
fortress, beer gardens, ...
Sun Excursion (few seats available!) 10.45 am Meet at main campus entry
Quarter final France vs. Island 9.00 pm Campus
Mon Free time Old town, fortress, beer
gardens, ...
Tue Barbecue 7 pm Campus
SommerUni party 9 pm Campus
Wed SommerUni barbecue 6 pm Campus

Thursday
Noshir Contractor:
Leveraging Web/Internet/Network
Sciences (WINS) to address
Grand Societal Challenges
9.00 am
10.30 am
Steffen Staab and Jérôme Kunegis:
Introduction to Web Science
3.00 pm
Poster session
6.00 pm
Cocktails at city beach
meeting point: main campus entrance
Breaks:
• 10 am,
• 12 am,
• 2.30 pm

Friday
Nikolaus Forgó:
Privacy Law and the Web: A Story
of Love and Hate?
9.00 am
10.30 am
Rüdiger Grimm:
Internet and Law
3.00 pm
Supervised project work
8.00 pm
SommerUni party
At the campus – impossible to miss!
Breaks:
• 10 am,
• 12 am,
• 2.30 pm

Thanks to our sponsors!

Let‘s get started!

Produce
Consume
Cognition
Emotion
Behavior
Socialisation
Knowledge
Observable
Micro-
interactions
in the Web
Apps
Protocols
Data & Information
Governance
WWW
Observable
Macro-
effects in the
Web
Web Science

Tertium Datur or Where Dijkstra was wrong
Computer
Science
Science
about
Computers
Astronomy
Telescope
Science
Web
Science
Science
about the
Web
What is Web Science?

Web science is an emerging interdisciplinary field
concerned with the study of large-scale
socio-technical systems, such as the World Wide
Web. It considers the relationship between people and
technology, the ways that
society and technology co-constitute one another
and the impact of this co-constitution
on broader society.
Wikipedia, 2016-06-29
Definition of Web Science

Agenda
• What is Web Science?
• What is the Web?
– Aspects of the Web at Large
• How to investigate the Web?
– Observing the Web
– An example using the architecture: bias in the Web
– How to model aspects of the Web
• What is the past and the future of the Web?

I try to
• classify, describe commonalities, find generalizations
I will not
• be 100% correct, 100% complete
BUT
• Please ask, suggest, complement,...
WHENEVER you feel like it!
DON‘T HESITATE ASKING!
A Note

What is the Web?

What is the Web: Aspects of the Web at Large
Architecture methodology:
• Draw pictures in different dimensions
• Pictures do not compete with, but complement each
other
Not only technical pictures!

The Web as a Device
Software
• Browsers
– IE, Firefox, Chrome,...
• Web Servers
– Apache, Tomcat,..
• Content Management
and Data Delivery
– Wordpress, drupal,
databases...
• Search Engines
• ...
Standards
• Uniform Resource
Locator (URL)
• HyperText Transfer
Protocol (http)
• HyperText Markup
Language (html)
• Domain name service
• ...+ many more

The Web as Content
For human consumpton
(primarily)
Text, Hypertext
Images
Video
Audio
Multimedia
Interaction (Games...)
Braille
Mathematics
For machine consumption
(primarily)
Metadata
Data
Ontologies

The Web as Content
Free
• Informational
• Advertisements
• Goods & services
• Web 2.0
– Wikipedia
– facebook
Paid
• Individual payments
• Subscription
• Micropayment
Freemium

The Web and its Stakeholders
People
• Citizens
• Customers
• Leisure seekers
• Workers
• Software developers
Internet providers
• Landline
• Mobile
• Nested providers
(internet cafe...)
• Website hosts
• Peer2peer networks (Bittorrent...)
Platform operators
• Shops
• News
• Web 2.0
• Payment
• Advertisement networks
• Trust centers
Government
• Police
• Military
• Secret service
• Law
• Citizen services
• Administration
• Politics

The Web as a Process
Governing
• Standards processes
– W3C
– Internet Engineering
Task Force (IETF)
– RFC
• Domain name
registration
• Internet routing
– E.g. „great Chinese
firewall“
Regulation
• Legal
– copyright
• where enforced
– hate speech
– ...
• Private
– E.g. Facebook,
Instagram ... Pictures,
nipple double standard

Observing the Web as
a Medium and Mirror of (anti-)social Practices
• (Self-)Expression
• Dark Web
– Crime
– Gold farming
– Violence
– Pornography
– Identity theft
• Sex lifes
– Fetishes
– prostitution
• Relationships
– Breakups
– Mobbing
– Stalking
– Advising
– Counseling
– Democracy

The Web as Medium & Mirror of
NEW (?) (anti-)Social Practices
Automation
• Twitter bots
• Online dating bots
• Smart contracts
(„Code is law“)
– E.g. DRM
• Quantified everything
– Physique
– Psyche
• Infer your Big 5
personality traits from
your facebook profile
• Threats to Internet
Security & Safety
– Hacking
– Social engineering

Produce
Consume
Cognition
Emotion
Behavior
Socialisation
Knowledge
Observable
Micro-
interactions
in the Web
Apps
Protocols
Data & Information
Governance
WWW
Observable
Macro-
effects in the
Web
Web Science: Discipline or Transdisciplinary Endeavour?

How to investigate the Web?

Web Observatories Konect

Why to observe?
• Understanding
– Collecting
– Describing
– Analyzing
– Modeling
– Predicting
– Repeating!

Challenges – Data Collection Issues
Legal and/or Ethical
• Crawling
– May be disallowed by provider
• Usage logging
– Privacy of individuals
• Even if it is allowed....

• Crawling
– What does it mean to crawl a heavily interactive site?
– Incomplete data
• Unreachability
• Time outs

• Crawling
– Incomplete data
– Where to start?
• We cannot observe everything!
– Even just for data size!
– What appear to be most fruitful starting points?

• Crawling
– Incomplete data
– Where to start?
– Where to stop?
• Each crawl is a view
– Twitter
» Tweet
• URL
• Web Page
• Subweb
» Followers
• Followers‘ Followers
• ...

• Crawling
– Incomplete data
– Where to start?
– Where to stop?
– Synchronous vs asynchronous
• Strictly speaking: only asynchronous crawling possible
– But in [Dellschaft&Staab] we targeted the construction of models for
streams of tags

Challenges – Data Publishing Issues
Legal and/or Ethical Example Issues
• AOL query log
• Netflix challenge
• Delicious
– http://www.tagora-project.eu/data/
• Twitter
– Collecting, but no sharing
• SocialSensor project

Challenges – Data Publishing Issues
Technical/Modelling issues
• Generic format, e.g. RDF
• Format ready for digestion by a certain software, e.g.
for Matlab processing
• Openness to other data
– E.g. references to DBPedia/Wikipedia
• Accuracy of publishing
– http://me.org showed „...“
– http://me.org showed „...“@2013-05-01:0900CEST
– http://me.org showed „...“@2013-05-01:0900CEST called
from IP 193.99.144.85 using browser...version...history...

Sharing Software
• Software
– For crawling or usage logging
– Rather than sharing the data, share the code for observing
• Example:
 code for crawling Twitter in a certain way
• Issues
– Limited repeatability
– Disturbance liability („Störerhaftung“) – at least in DE
• If you provide source code for crawling, e.g., Facebook, even if you
do not crawl FB, FB can sue you

More later by Jerome

Model the Web
What is in the
content?
What is in the
algorithm?
What is in the
Social machine?
WebObservatory

Example Topic: Bias

Bias in the Device
Accessibility
• Impaired eyesight
• Impaired use of
mouse and keyboard
• ...
HTML5 semantics helps
– but is not used much

Bias in the Device
Accessibility
• Impaired use of
mouse and keyboard
• ...
HTML5 semantics helps
– but is not used much
Haris Aslanidis

Bias in the Device
Accessibility
• Impaired use of mouse and
keyboard
• ...
HTML5 semantics helps – but is
not used much
http://west.uni-
koblenz.de/en/research/projects/
mamem
http://www.mamem.eu/mamem-
makes-available-gazetheweb-
browse-application/

Search engines
• Categorizing people and animals
– White vs black
– http://www.nytimes.com/2016/06/26/opinion/sunday/artificia
l-intelligences-white-guy-problem.html?_r=0
• Job advertisements
– Well-paid job not offered to females
Bias in the Software

Bias in Content/Data
Credit Hire Sex Ethnic Zip Height ... ...
+ +
+ -
- +
+ +
- -
correlated
Data protection laws
suggest not to process
sensitive data attributes
like „sex“ or „ethnic“

Bias in Content: Social Networks
(Lerman et al 15)

fish, rice
seafood, fish seafood, shrimp lobster, wine
seafood, fish, salmon
fish, salmon, wine
rice, fish
lobster, seafood, shrimp
coffee
coffee, wine
coffee
wine
wine
pizza, wine
pizza, wine
pasta, wine
pasta, shrimp
lobster, shrimp
seafood, shrimp
Tagged photos with geo-coordinates from Flickr

fish, rice
seafood, fish seafood, shrimp lobster, wine
seafood, fish, salmon
fish, salmon, wine
seafood, shrimp
lobster, seafood, shrimp
coffee
coffee, wine
coffee
italian, wine
wine
pizza, wine
italian, pizza, wine
pasta, wine
pasta, shrimp
seafood
fish
lobster
shrimp
crab
wine
salmon
wine
pizza
coffee
italian
pasta
seafood, shrimp
lobster, shrimp
Bias in the Algorithm: Shape of Clusters

Evaluation: Anectodal, Perplexity, Gaming
Gaming study:
intrusion detection
Precision 8 topics
avg / median
LGTA 0.60 / 0.58
Basic model 0.64 / 0.58
MGTM 0.78 / 0.75

• The Web reflects current and past discrimination!
• Example:
– Predict who will leave the company based on email
features (ICWSM-16)
• People with outsiders‘ vocabulary more prone to fail
• Unresolved:
– Do they fail because they are less adaptable or
– Do they fail because the environment is hostile to outsider?
Bias and Social Practices

Wikipedia
• Efforts to counter bias
Law
• E.g. UK equality act
• Protected characteristics:
– Age, disability, gender, marriage, religion,...
Bias and Processes

Biases in the Social Machine:
The Case of Liquid Feedback

...

Online Delegative Democracy
CC-BY-SA Ilmari Karonen

Delegative Democracy
• Between direct and representative democracy

• Voters can delegate their vote to other voters

• Delegations can be revoked at any time

• Delegations can be revoked at any time
• Votes are public!

Dataset:
LiquidFeedback
(German Pirate Party)

LiquidFeedback – Pirate Party
• Observation: 08/2010 – 11/2013
• 13,836 Members
• 14,964 Delegations
• 499,009 Votes

LiquidFeedback – German Pirate Party
•
Users create initiatives, which are grouped by
issues and belong to areas

•
Area: Environmental issues
Issue: CO2 output has to be reduced.
Initiative: Subsidise wind turbines!

•
Area: Environmental issues
Issue: CO2 output has to be reduced.
Initiative: Subsidise wind turbines!
Areas: 22
Issues: 3,565
Initiatives: 6,517

•
Delegations on global, initiative, issue
and area level
→ “Back-delegations” possible

Dataset – First Impressions
•

Dataset – First Impressions
•
Voting Weight

Dataset – Bias ?
•
3,658 members > 10 votes
1,156 members > 100 votes
54 members > 1,000 votesMedian all: 8 votes
Median delegating: 42 votes
Median delegates: 64 votes

Delegation Network
• Temporal analysis
•

Delegation Network
•

Investigation
• What are the social processes?
• How are data collected?
• Which algorithms are used?
• How is the algorithm used in system processes?
• What are the system effects?
Purposes of investigation
• Social sciences: Observe bias in action!
• Intervention: Change the overall system!
• System sciences: Understand system limits!
• Engineering: Build „proper“ technical system!
Summary: Bias

Web Models

• Descriptive
– Qualitative
– Statistical
• Predictive
– Modeling deterministic regularities
• Generative
– Modeling non-deterministic principles
• Liking a song
• Creating a link
Web Models

Descriptive Models
Example:
Bow Tie Structure of the Web

Bow-tie structure of the Web

Predictive Models
Example:
Link Prediction by Triangle Closing

Social Network
Person Friendship

Recommender Systems
Predict who I will add as friend next
Standard algorithm: find friends-of-friends
me

Friend of a Friend
1 2 4 5 6
3
Count the number of ways a person can be
found as the friend of a friend.

Generative Models
Example:
Link Creation by Barabasi-Albert

• Many large networks are scale free
– Matthew effect: rich get richer
• Vote delegation!
• The degree distribution has a power-law behavior for
large k (far from a Poisson distribution)
• Random graph theory and the Watts-Strogatz model
cannot reproduce this feature
General considerations

Scale free
Same shape of hull,
no matter which resolution

Scale-free of Web distribution
Same shape of distribution: no matter which k

Two generic mechanisms common in many real
networks:
• Growth (www, research literature, ...)
• Preferential attachment:
attractiveness of popularity
The two are necessary
Barabasi-Albert model (1999)
Barabási & Albert, Science 286, 509 (1999)

• t=0, m0 nodes
• Each time step we add a new node with m ( m0)
edges that link the new node to m different nodes
already present in the system
Growth

• When choosing the nodes to which the new
connects, the probability that a new node will be
connected to node i depends on the degree ki of
node
Preferential attachment
( ) i
i
j
j
k
k
k
 

Linear attachment (more general models)
Sum over all existing nodes

Numerical simulations
• Power-law P(k) k-
SF=3
• The exponent does not depend on m (the
only parameter of the model)

The Past and the Future Web

• 1945 Vannevar Bush, „As we may think“, Memex
• 1962 Ted Nelson, Hypertext
• 1965 Wide area network
• 1968 Doug Engelbart, The mother of all demos
• 1972 Public Arpanet, Email
• 1974-82 Internet protocol TCP/IP
• 1978 Consumer information services & Email
• 1983 AOL, online service for games, communities...
• 1984 Domain name service
Pre-Web

The World Wide Web
• 1989 Concept drafted by Tim Berners-Lee
• 1993 National Center for SuperComputing
Applications launched Mosaic X
• 1994 First WWW conference
• 1994 W3C started at MIT
• Commercial websites began their proliferation
• Followed by local school/club/family sites
• The web exploded
– 1994 – 3,2 million hosts and 3,000 websites
– 1995 – 6,4 million hosts and 25,000 websites
– 1997 – 19,5 million hosts and 1,2 million websites
– January 2001 – 110 million hosts and 30 million websites

The World Wide Web
– 1994/1995 Amazon
– 1994/1995 Wiki
– 1995 AltaVista Search Engine
– 1995 Internet Explorer
– 1997-2001 Browser wars
– 1996-1998 XML recommendation
– 1998 Google
– 1999 First W3C recommendation on RDF (Semantic Web)
– 2001 Dot.Com bubble bursts
– 2001 Wikipedia
– 2003/2004 Facebook
– 2004 Flickr
– 2005 YouTube

Concepts Example Applications
Web of People Physical transport service (Uber (2009),
Lyft), accommodation service (AirBnB,
Couchsurfing), online dating service,
2009
Web of Things Smart city, ambient intelligence, personal
and public health information, personal
and public transport information
2007
Web of Services Cloud services, Digital transformation,
Programmable Web (2005)
2005
Web of Data User generated content applications
(Facebook, Wikipedia (2001) and
Wikidata,…), Linked open gov data
2001
Web of Documents HTTP, HTML, XML, Browser (Mosaic
1993)
1993
Computer
Networks/Internet
Document delivery (internet 1982), VOIP,
Streaming
1982

Internet of Things vs Web of Things
IoT
• Internet
• P2P networks
• Sensors
Web of things
• How to link?
• What is „linking WoT“?
• How to use by me?
• How to discover?
• How to search?

Web of People

http://static.tapastic.com/cartoons/19/f5/11/a0/a70ea5d7dbdd477d9e3bc2e0a2bfa286.gif

Aaron Swartz †
Co-author of RSS1.0 at the age of 14

http://static.tapastic.com/cartoons/19/f5/11/a0/a70ea5d7dbdd477d9e3bc2e0a2bfa286.gif

Web of People
• What is it?
– Identification
• Orcid
• Facebook ID
• Oauth 2.0
?
– Trust
• Uber score
• Airbnb score
• Ebay score
• Tinder score
• Group score
– Chinese social score
– Credit rating score
?

Concepts Delivered technical capabilities
Web of People Identification and rating by/of people
Web of Things Identification, linking, aggregation,
monitoring and controlling of things
Web of Services Identification, composition and calling of
services
Web of Data Identification, linking and retrieval of data
Web of Documents Identification, linking and retrieval of
documents
Computer
Networks/Internet
Identification of and communication
between computers

Concepts Standards
Web of People No mature standards yet
Web of Things No mature standards yet
Web of Services REST, JSON, JSONLD
Web of Data RDF, SPARQL
Web of Documents HTTP, HTML, XML, AJAX
Computer networks/Internet Internet, TCP/IP, Optical fibre, 5G

Your thoughts?

Conclusions

What is in the
data?
What is in the
algorithm?
What is in the
Social machine?
WebObservatory

Telling the Story with Web Science
What is in the
Data?
What is in the
Algorithm?
What is in the
Social Machine?
Story telling
Under-
standing
Modelling

Web
Accomplishments
• Web of Services
• Web of Data
• Web of Documents
• Computer
networks/Internet
Future in the making
• Web of People
• Web of Things
How to identify and use?
How to observe?
How to resolve issues?

Thank you!

Wwsss intro2016-final

Recommandé

Recommandé

Contenu connexe

En vedette

En vedette (6)

Similaire à Wwsss intro2016-final

Similaire à Wwsss intro2016-final (20)

Plus de Steffen Staab

Plus de Steffen Staab (20)

Dernier

Dernier (17)

Wwsss intro2016-final