Social Computing - Intro

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8/28/2009

Social Computing: a New Interdisciplinary Study Julita Vassileva Computer Science Department University of Saskatchewan 1

What is Social Computing? •



Social computing is a social structure in which technology puts power in  communities, not institutions. As more individuals use the Internet to shop, work,  and exchange ideas, a more egalitarian social structure is emerging. Individuals  g g g g take cues from one another, rather than traditional sources of authority — like  corporations, media outlets, political institutions or organized religions.  Manifestations of social computing include:

• • • • • • • • • • • • • • •

Social networks  Peer‐to‐peer content distribution  Open source software  Blogs  RSS  Podcasting  Consumer‐to‐consumer commerce  Meet‐ups  Mash‐ups  Tagging  Social search  User‐generated content  Peer ratings  Wikis  Comments and trackbacks  Widgets 

• •

Voter‐driven content  (Forrester Research, 2008) http://www.forrester.com/ResearchThemes/SocialComputing

Key "tenets of social computing" outlined by Charlene Li: •innovation will shift from top‐down to bottom‐up  •value will shift from ownership to experience  l ill hift f hi t i •power will shift from institutions to communities •http://www.socialcustomer.com/2006/02/the_forrester_s.html

2/25

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Complex  Systems Sociology,  Anthropology h l

Computer Science, Web Social Computing

Decision Making,  Politics, Education

Social Psychology Behavioral Economics

3/25

Computer Science • Social Computing evolved as a way of  i t interacting and collaborating on the web ti d ll b ti th b

4/25

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Social Sciences • Analyzing the interactions in communities • Observing social phenomena  – hazing of newbies in forums (e.g. X‐Files fans) C. Honeycutt (2005) Hazing as a Process of Boundary Maintenance in an Online Community

– reputation /power economy of Wikipedia (similar to that of research community) A.Forte, A.Bruckman (2005) Why do people write for Wikipedia? Georgia Tech Report

5/25

Behavioral Economics • Why do people behave irrationally /   altruistically? lt i ti ll ? • Money‐economy vs. social norms – E.g. try to pay your mother‐in‐law for the lovely  Thanksgiving dinner she cooked for the family  – Reciprocation (immediate, delayed, concrete,  p ( , y , , generalized) – Gift economies Dan Ariely (2007) Predictably Irrational 6 / 25

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Social Psychology  • Individual motivations for contribution – Many theories can explain observed behavior – Can a theory be used as a guideline in system  design to ensure motivation?  Rob Kraut (2005) Social Psychology & Online communities

– Exploring the effect of visualization according to  Exploring the effect of visualization according to certain theories in different communities • Social comparison theory  in Comtella • Common identity theory  in WISETales • Common bond theory 7/25

Incentive: Status/Reputation Customer Loyalty Programs

Image from  depts.washington.edu/.../painting/4reveldt.htm Cheng R., Vassileva J. (2006) Design and Evaluation of an Adaptive Incentive Mechanism for Sustained Educational Online Communities. User Modelling and User-Adapted Interaction, 16 (2/3), 321-348.

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Immediate gratification for rating

Topics and individual postings that are rated higher appear “hot”, those rated lower appear “cold” Æ colours ease navigation in the content Æ aesthetically pleasing, intuitive

Webster A.S., Vassileva J. (2006) Visualizing Personal Relations in Online Communities, Proceedings Adaptive Hypermedia and Adaptive Web-Based Systems, Dublin, Springer LNCS 4018, 223-233. 10/25

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Sahib, Z., Vassileva J. (2009) Designing to Attract Participation In A Niche Community For Women In Science & Engineering, in Proc.WS Social Computing in Education, with the 1st IEEE International Conference on Social Computing, SocialComp'2009, Vancouver, BC, August 29-31, 2009.

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Common bond ‐ reciprocation

Raghavun, K., Vassileva J. (2009) Visualizing Reciprocal and non-Reciprocal Relationships in an Online Community. Proc. Workshop on Adaptation and Personalization for Web 2.0, in conjunction with UMAP 2009, June 22-26, 2009, Trento, Italy. 12/25

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Business/Organizational Studies • How do groups make decisions?  • Features of groups that make good decisions:  diversity, decentralization, independence,  aggregation • Phenomena: cascades, social norms, group think, • Interactions: fairness, punishment, trust

Cass Sunstein (2007) Infotopia James Surowiecki (2007) The Wisdom of Crowds 13/25

How are small groups different from  wise crowds? • People think of themselves as members of a team, while  in a market, they think of themselves as independent  actors.  • The group has an identity of its own – Consensus is important for the existence and comfort of the  group – Influence of the people in the group on each other’s judgment is unavoidable. – Group polarization Group polarization

• Collective wisdom, in contrast, is something that emerges  as a result of many different independent judgments, not  something that the group should consciously come up  with.  14/25

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Consequences • Small cohesive groups / communities may be  wrong or biased (encapsulation) • Does this apply to online groups ? • Currently we see tagging, voting (rating) systems  and recommenders emerge as forms of “collective  wisdom” online

• O Open question: what can designers do to  i h d i d avoid biases resulting from activities of small  groups online?  15/25

Importance of mechanism • A decentralized system can only produce intelligent results if  there is a means of aggregating the private information of  there is a means of aggregating the private information of everyone • An aggregation mechanism is a form of centralization, (ideally)  of all the private information of the participants – provides incentives for revealing truthfully private info – should not inject extra bias in the system Mechanisms:  New mechanisms: – One person with foresight - Prediction markets - Trust and reputation – Deliberation mechanisms – Polls / votes – Price in a open market 16/25

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Complex, self organizing systems

N(k) ‐ # pages with K incoming links N(k) ~ k –γ , where γ – degree exponent,  in this case γ = 2.5

Many empirically observed networks appear to be scale-free: world wide web, protein networks, citation networks, and some social networks. 17/25

Scale Free Networks • Macroscopic effects of individual behaviour – emerging patterns (Barabási & Albert, 1999) – Growth and preferential attachment explain the hubs and  power laws in complex networks, like the Web; 

• Fitness of a node in a competitive environment • The “Fit get rich” model (borrowing formalisms from  quantum mechanics) predicts a phenomenon called  Einstein‐Bose condensation • In some networks (under special conditions) all links will  ultimately point to one node: “The winner takes it all” or

18/25

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Robust Scale Free Networks • Scale‐free networks are extremely robust in case of  random failures random failures • Studying network resilience – In random networks, some node failures can easily break a  network into isolated, non‐communicating parts.  – Yet, a study of the Internet resilience showed that we can  remove 80% of all nodes, and the remaining 20% will still  , g remain connected – The key to this is the presence of hubs, removing nodes  randomly is not likely to affect them, and they hold the  NW together 19/25

Vulnerable Scale Free Networks • Yet, scale‐free NW are very vulnerable to  g g targeted attacks and to cascading failures • In case of targeted attack on a critical number  of hubs, the network disintegrates very quickly • Cascading failures – examples  – Power grid black outs (1996, 2003) – Cascades of malfunctioning routers on the Internet – Cascading East Asian economic  crisis in 1997 – Cascades in ecological habitats 20/25

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Consequences • The laws of power networks lead to  concentration  – clear targets that need to be protected  – less diversity (or lesser impact of diverse opinion), less  creativity – more power (network power,  $$$s, legal advisors and  lobbyists) in very few hands – possibility of  possibility of “locking locking up up” the internet by a couple of  the internet by a couple of corporate giants • Creeping copyright protections (patents, DRM)  • Apple locking up the iPhone 21/25

Spreading Viruses and Innovation • Viruses • Innovation

# adopters

• Hubs:  – – – –

Opinion leaders time Power users Laggards Innovators Hubs Mass Influencers Are not necessarily innovators, but they are key to spreading  y , y y p g an innovation, launching an idea…. 

• Yet, not all innovations catch on (e.g. Apple’s Newton).  Why some do and some do not? • Diffusion models  22/25

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Disease diffusion models • Threshold model: Each innovation has  – spreading rate – the likelihood that it will be adopted by a person  introduced to it, and introduced to it, and  – critical threshold – defined by the properties of the NW in which the  information spreads – If spreading rate < critical threshold, it will die,  Else, the number of people adopting the innovation will increase  exponentially. 

• This model has been  used by epidemiologists,  marketers, sociologists, political scientists – but it doesn’t explain the persistence of some viruses like AIDS – It assumes a random network topology.  – In scale‐free topology, the critical threshold disappears.  23/25

Consequences  • Ideas can be spread  very quickly and far in a  scale free network l f t k • Political ideas, innovations, but also radical /  extremist ideas • Action can be organized very quickly – E.g.  E g “flash flash‐crowds crowds” with Twitter with Twitter

• Are we prepared to deal with this?   • What is the impact on education? 24/25

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Some food for thought…  “While entirely of human design, the Internet now lives a  life of its own. It has all the characteristics of a  f f f complex evolving system, making it more similar to a  cell than a computer chip. Many diverse components,  developed separately, contribute to the functioning of  a system that is far more than the sum of its parts.  Therefore Internet researchers are increasingly  morphing from designers into explorers. They are like  bi l i biologists or ecologists who are faced with an  l i h f d ih incredibly complex system that, for all practical  purposes, exists independently of them.” (pp.149‐150) Albert‐László Barabási, Linked, Plume Publ. 2003. 25/25

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