lecture 7: social web challenges (2012)

Social WebLecture VII

What are the CHALLENGES on the Social Web?

Lora AroyoThe Network Institute

VU University Amsterdam

(based on article by Won Kim, Ok-Ran Jeong and Sang-Won Lee)

Monday, March 19, 12

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


• 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


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




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, 2002Monday, March 19, 12

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

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

it’s relationships, stupid!not attributes

May, 2007April, 2002

All the world's a netby David Cohen


http://www.newscientist.com/search?rbauthors=David+Cohen

http://www.newscientist.com/search?rbauthors=David+Cohen

NYT, 26 February 2007


Do we have the same rules online and offline?


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


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


Networks in Organizations

It’s Networks all the way down, and up…

Nigel Shadbold, slides 2010Monday, March 19, 12

Collective Intelligence

• why do people contribute?

• how to maintain the connected content?

• how are trust & provenance represented, maintained and repaired on the Web?


Collective Intelligence

Motivation Example MeanFun “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


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?


How can we discern between good and bad on the SW?


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


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

★★ ★★ ★ ★★ ★ ★ ★ ★ ★ ★ ★ ★


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


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?


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?


How is doing good with social media affected by

infrastructure?


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?


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


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


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


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’’


• 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”

Law & Technology


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


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?


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?


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


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


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/


http://www.flickr.com/photos/bionicteaching/1375254387/

http://www.flickr.com/photos/bionicteaching/1375254387/

lecture 7: social web challenges (2012)

Technology

social web

collective

social web

sciences amp

south korea

nigel shadbold

slides 2010monday

data