This is Lecture VII: What are the CHALLENGES on the Social Web? as part of the Social Web course at the VU University Amsterdam. Visit the website for more information: http://semanticweb.cs.vu.nl/socialweb2012/ Lora Aroyo, The Network Institute, VU University Amsterdam (some slides based on article by Won Kim, Ok-Ran Jeong and Sang-Won Lee)

1. Social Web
Lecture VII
What are the CHALLENGES on the Social Web?
Lora Aroyo
The Network Institute
VU University Amsterdam
(based on article by Won Kim, Ok-Ran Jeong and Sang-Won Lee)
Monday, March 19, 12

2. Previously
on the social web ...
• modeling - the Web Graph
• mining & visualization - hands-on
• but important for the future agenda is to
consider the right ‘issues’ to model & mine
• challenges
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3. • Leveraging recent advances in:
• Theories: about the social motivations for
creating, maintaining, dissolving and re-creating
links in multidimensional networks and about
emergence of macro-structures
• Data: Semantic Web/Web 2.0 provide the
technological capability to capture, store, merge,
and query relational metadata needed to more
effectively understand and enable communities
• Methods: qualitative and quantitative
methods to enable theoretically grounded
network predictions
• Computational infrastructure: Cloud
computing and petascale applications are
critical to face the computational challenges in
analyzing the data
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4. 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)
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5. Think Networks!
Albert-László Barabási: Linked:The New Science of 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.
April, 2002
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6. it’s relationships, stupid!
not attributes
All the world's a net
by David Cohen
April, 2002 May, 2007
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7. NYT, 26 February 2007
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8. Do we have the same rules online and offline?
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9. 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 who
you know, it’s what they think you know
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10. Shift to Team Science
• Studies of 19.9 million research articles over 5
decades (in the Web of Science database) and an
additional 2.1 million patent records (1975-2005)
found three important facts:
• for all fields, research is increasingly done in teams
• teams produce more highly cited research than
individuals do
• high impact research is also done in teams now
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11. Networks in
Organizations
It’s Networks all the way down, and up…
Nigel Shadbold, slides 2010
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12. Collective Intelligence
• why do people contribute?
• how to maintain the connected
content?
• how are trust & provenance
represented, maintained and
repaired on the Web?
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13. Collective Intelligence
Motivation Example Mean
Fun “Writing in Wikipedia is fun” 6.10
Ideology “I think information should be free” 5.59
Values “I feel it’s important to help others” 3.96
Understanding “Writing in Wikipedia allows me to gain a new perspective on things” 3.92
Enhancement “Writing in Wikipedia makes me feel needed” 2.97
Protective “By writing in Wikipedia 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 in Wikipedia” 1.51
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14. Challenges for
Collective Intelligence
• What are means to come from collective
intelligence to collaborative innovation?
• How to manage the risks & problems of
community-generated information
resources?, e.g. WikiLeaks
• What legal frameworks (should) apply to
collective intelligence?
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15. How can we discern between good and bad on the SW?
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16. Open Data
• common standards for release of
public data
• common terms for data where
necessary
• licenses - CC variants
• exploitation & publication of
distributed and decentralized
information assets
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17. Monday, March 19, 12

18. 5 star rating scheme
linked open data
★ Available on the web (whatever format), open license
★★ Available as machine-readable structured data (e.g. excel)
Use non-proprietary format (e.g. CSV instead of excel)
★★★
Use open standards from W3C (e.g. RDF, SPARQL) to
★★★★ identify things, so that people can point at your stuff
★★★★★ Link your data to other people’s data to provide context
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22. Open Data Challenges
• Data hugging culture
• License impediments
• Worries about:
• confidentiality
• interpretations of data, e.g. liability
• quality of information, e.g. accuracy, reputation
• disrupted workflow, e.g. additional work
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23. Large Linked Data
• how to browse, explore & query?
• how to support inference at a Web scale?
• what reasoning & context representation is
possible?
• how to identify and what to do with necrotic
& non-functional parts of the Web?
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24. Socio-Technical
• How to do mixed methods research
to explore the relations between
ethnographic insights to Web practice?
• How to draw on new data sources
e.g. digital records of network use to
develop understanding of the sociological
aspects of the Web?
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25. How is doing
good with social
media affected by
infrastructure?
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26. Two Cultures
• “the breakdown of
communication between the "two
cultures" of modern society - the
sciences & the humanities - was a
major hindrance to solving the
world's problems” [CP Snow - Rede
Lecture 1959]
• understanding and advancing the
Social Web implies research in
sciences & humanities
• what do we need to know and
understand of each others methods?
Nigel Shadbold, slides 2010
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27. Nigel Shadbold, slides 2010
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28. Big Data Owners
Who 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
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29. Economics & Technology
• What are the economics of Web
2.0 & Web 3.0?
• commercial incentives & industrial
structure created by the Web
• economic arguments for and
against open platforms in the Web
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30. Profitability
• many profitable social Web sites
• LinkedIn charges fees for job postings
• LinkedIn charges for hosting closed social networks for
businesses, expert search services, and banner ads
• most generate revenue by selling online ads, virtual gifts,
ringtones, artist merchandise, concert tickets
• many try to rapidly increase traffic to their sites/ number
of members, however not equally expensive everywhere
in the world
• need to modify business strategy over time, as the
demographics of the members evolve, and unforeseen
situations appear
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31. Online Ads
• not as much online advertising revenues
• inherent difficulties of targeted advertising on social Web
• users of social Web sites are not looking for information
to buy things
• not willing to have their friends become targets of online
ads
• businesses don’t want their ads placed on quirky social
groups
• failed attempts: Facebook’s advertising program called
Beacon - tracked all Facebook users’ purchases (eBay)
and displayed them to all of their Facebook ‘‘friends’’
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32. Law & Technology
• representing & reasoning over legal and
social rules
• Should law be a catalyst for change or merely
reactive to it?
• What is content on the Semantic Web
(“computer generated”) and what rights
should attach to it?
• Which technologies within the Web
should the law ensure remain “open”?
• Should service providers be legal gatekeepers
for public authorities?, e.g. Web policing for
“illegal and harmful content”
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33. Legal Issues Examples
• People who do not abide by laws create legal problems
not only for themselves but also the sites they use
• Social Web sites have been used as platforms for
organizing anti-government dissents in various countries,
e.g. South Korea, France, Egypt, China
• many users post copyrighted materials without
authorization, also pornographic materials, materials that
may violate privacy laws, etc.
• it’s practically impossible for the site operators to
remove such materials before they are viewed and
spread on the Internet
Monday, March 19, 12

34. Security, Privacy & Trust
• How and why do they break down?
• Does activity in the Digital Economy change if
they do?, e.g. DigID, credit cards, censorship
• How can trust be repaired? Is it stronger once
repaired?
• Can we make them more machine
processable?, e.g. SSL, HTTPS, OpenID
• a new state of global hypersurveillance
• our electronic activity leaves digital footprints
• miniature witnesses, forming powerful
networks whose emergent behaviour can be
very complex, intelligent, and invasive
• how much of an infringement on privacy are
they?
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35. Open & Private Web:
Values & Rights
• a mix of open/public areas & closed/private zones
• openness on the Web needs to be governed, e.g. legal
frameworks needed
• economic & legal issues dominate innovation
• Does innovation follow from openness or is it a result of
private and commercial incentives?
• How to move from open to a more restrictive business model?
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36. Damage to self
• usually based on benign believes
• college applicants, job seekers, criminals,
court cases ... others can see your friends
as well
• signs of addiction
• reduced productivity, e.g. corporations
restrict access to Facebook
• ending of life, i.e. suicide Web sites
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37. Damage to others
• hiding behind online identifies
• no means of validating accuracy of personal
profiles, e.g. email only
• spreading false rumors, e.g. mad cow disease in
South Korea, Apple stock falling 15%
• cyber bullying and cyber stalking - leading to
suicides
• pornographic material, politically sensitive
materials
• evaluate accuracy and trustworthiness of news articles
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38. Final Assignment
Presentations
• consider carefully the issues from the last
two lectures
• consider the presentation as a detailed plan
for prototyping your application
• motivate the need for this app & its goal
• present details about your data
• motivate the choices you made
• present the approach your app applies, e.g.
what clustering, mining, visualization, etc.
image source: http://www.flickr.com/photos/bionicteaching/1375254387/
Monday, March 19, 12