Social Computing - Intro

8/14/2019 Social Computing - Intro

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SocialComputing:

aNewInterdisciplinaryStudy

JulitaVassileva

ComputerScience

Department

UniversityofSaskatchewan

1

Whatis

Social

Computing?

Socialcomputingisasocialstructureinwhichtechnologyputspowerincommunities,notinstitutions.AsmoreindividualsusetheInternettoshop,work,andexchangeideas,amoreegalitariansocialstructureisemerging.Individualstakecuesfromoneanother,ratherthantraditionalsourcesofauthority likecorporations,mediaoutlets,politicalinstitutionsororganizedreligions.Manifestations ofsocialcomputinginclude:

Socialnetworks

Peertopeercontentdistribution

Opensourcesoftware Blogs RSS Podcasting Consumertoconsumercommerce Meetups Mashups

Key"tenetsofsocialcomputing"outlinedbyCharleneLi:

innovationwillshiftfromtopdowntobottomup

Tagging

Socialsearch Usergeneratedcontent Peerratings

Wikis Comments andtrackbacks Widgets Voterdrivencontent (Forrester Research,2008)http://www.forrester.com/ResearchThemes/SocialComputing

va ue

w

s

rom

owners p

o

exper ence

powerwillshiftfrominstitutionstocommunitieshttp://www.socialcustomer.com/2006/02/the_forrester_s.html

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Complex

Systems

Computer Sociology,

SocialPsychology

BehavioralEconomics

DecisionMaking,

Politics,

Science,Web

SocialComputingSocialComputingAnthropology

Education

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ComputerScience

SocialComputingevolvedasawayofnteract ngan co a orat ngont ewe

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SocialSciences

Analyzingtheinteractionsincommunities

Observingsocialphenomena

hazingofnewbies inforums(e.g.XFilesfans)C. Honeycutt (2005) Hazing as a Process of

Boundary Maintenance in an Online Community

reputation/power

economy

of

Wikipedia

(similartothatofresearchcommunity)A.Forte, A.Bruckman (2005) Why do people write

for Wikipedia? Georgia Tech Report5/25

BehavioralEconomics

Whydopeoplebehaveirrationally/a tru st ca y

Moneyeconomyvs.socialnorms

E.g.trytopayyourmotherinlawforthelovelyThanksgivingdinnershecookedforthefamily

Reci rocation immediate dela ed concretegeneralized)

Gifteconomies

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Dan Ariely (2007) Predictably Irrational


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SocialPsychology

Individualmotivationsforcontribution

Manytheoriescanexplainobservedbehavior

Canatheorybeusedasaguidelineinsystemdesigntoensuremotivation?Rob Kraut (2005) Social Psychology & Online

communities

certaintheoriesindifferentcommunities

Socialcomparisontheory inComtella Commonidentitytheory Commonbondtheory in WISETales

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Incentive:Status/Reputation

CustomerLoyaltyPrograms

Imagefrom

depts.washington.edu/.../painting/4reveldt.htm

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Cheng R., Vassileva J. (2006) Design and Evaluation of an Adaptive Incentive Mechanism for SustainedEducational Online Communities. User Modelling and User-Adapted Interaction, 16 (2/3), 321-348.


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Immediategratificationforrating

Topicsandindividualpostingsthatareratedhigherappearhot,thoseratedlowerappearcold colours easenavigationinthecontent aestheticallypleasing,intuitive

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Webster A.S., Vassileva J. (2006)Visualizing Personal Relations in OnlineCommunities, Proceedings AdaptiveHypermedia and Adaptive Web-BasedSystems, Dublin, Springer LNCS 4018,

223-233.


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

Commonbond

reciprocation

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


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Business/OrganizationalStudies

Howdogroupsmakedecisions?

Featuresofgroupsthatmakegooddecisions:diversity,decentralization,independence,

aggregation

Phenomena:cascades,socialnorms,groupthink,

Interactions:fairness,punishment,trust

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Cass Sunstein (2007) Infotopia

James Surowiecki (2007) The Wisdom of Crowds

Howaresmallgroupsdifferentfrom

wisecrowds? Peoplethinkofthemselvesasmembersofateam,while

inamarket,theythinkofthemselvesasindependentactors.

ThegrouphasanidentityofitsownConsensusisimportantfortheexistenceandcomfortofthe

group

Influenceofthepeopleinthegrouponeachothersjudgmentisunavoidable.

Collectivewisdom,incontrast,issomethingthatemergesasaresultofmanydifferentindependentjudgments,notsomethingthatthegroupshouldconsciouslycomeupwith.

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Consequences

Smallcohesivegroups/communitiesmaybewron orbiased enca sulation

Doesthisapplytoonlinegroups?

Currentlyweseetagging,voting (rating)systemsandrecommenders emergeasformsofcollective

wisdomonline

penquest on:w atcan es gners otoavoid

biases

resulting

from

activities

of

small

groupsonline?

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Importanceof

mechanism

Adecentralizedsystemcanonlyproduceintelligentresultsifthere is a means ofa re atin the rivate information of

everyone

Anaggregationmechanismisaformofcentralization, (ideally)ofalltheprivateinformationoftheparticipants

providesincentivesforrevealingtruthfullyprivateinfo

shouldnotinjectextrabiasinthesystem

Mechanisms:

Onepersonwithforesight

Deliberation

Polls/votes

Priceinaopenmarket

New mechanisms:- Prediction markets- Trust and reputation

mechanisms

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Complex,selforganizingsystems

Many empirically observed networksappear to be scale-free: world wide web,

protein networks, citation networks,

and some social networks.

N(k) #pageswithKincominglinks

N(k)~k , where degreeexponent,

inthiscase = 2.5

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ScaleFree

Networks

Macroscopiceffectsofindividualbehaviour emergingpatterns(Barabsi &Albert,1999)Growth andpreferentialattachmentexplainthehubsand

powerlawsincomplexnetworks,liketheWeb;

Fitness ofanodeinacompetitiveenvironment TheFitgetrichmodel(borrowingformalismsfrom

quantummechanics)predictsaphenomenoncalled

EinsteinBose

condensation

Insomenetworks(underspecialconditions)alllinkswillultimatelypointtoonenode:Thewinnertakesitall

or18/25


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RobustScaleFreeNetworks

Scalefreenetworksareextremelyrobustincaseof

Studyingnetworkresilience

Inrandomnetworks,somenodefailurescaneasilybreakanetworkintoisolated,noncommunicatingparts.

Yet,astudyoftheInternetresilienceshowedthatwecanremove80%ofallnodes,andtheremaining20%willstill

remainconnected

Thekey

to

this

is

the

presence

of

hubs,

removing

nodes

randomlyisnotlikelytoaffectthem,andtheyholdthe

NWtogether

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VulnerableScale

Free

Networks

Yet,scalefreeNWareveryvulnerabletotar etedattacksandtocascadin failures

Incaseoftargetedattackonacriticalnumberofhubs,thenetworkdisintegratesveryquickly

Cascadingfailures examples

Powergridblackouts(1996,2003)

Cascadesof

malfunctioning

routers

on

the

Internet

CascadingEastAsianeconomic crisisin1997

Cascadesinecologicalhabitats

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Consequences

Thelaws

of

power

networks

lead

to

concentrationcleartargetsthatneedtobeprotected

lessdiversity(orlesserimpactofdiverseopinion),lesscreativity

morepower(networkpower, $$$s,legaladvisorsandlobbyists)inveryfewhands

corporategiants

Creepingcopyright

protections

(patents,

DRM)

ApplelockinguptheiPhone

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SpreadingViruses

and

Innovation

Viruses

Innovation Hubs:

Opinionleaders

Powerusers

Influencers

Arenotnecessaril innovators,butthe areke tos readin

Innovators Hubs Mass Laggards

# adopters

time

aninnovation,

launching

an

idea.

Yet,notallinnovationscatchon(e.g.ApplesNewton).Whysomedoandsomedonot?

Diffusionmodels

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Diseasediffusionmodels

Thresholdmodel:

Each

innovation

has

spreadingrate thelikelihoodthatitwillbeadoptedbyapersonintroduced to it and

criticalthreshold definedbythepropertiesoftheNWinwhichtheinformationspreads

Ifspreadingrate


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Somefoodforthought

Whileentirelyofhumandesign,theInternetnowlivesali eo itsown.Ithasallthecharacteristicso acomplexevolvingsystem,makingitmoresimilartoacellthanacomputerchip.Manydiversecomponents,developedseparately,contributetothefunctioningofasystemthatisfarmorethanthesumofitsparts.ThereforeInternetresearchersareincreasinglymorphingfromdesignersintoexplorers.Theyarelike

o og stsoreco og stsw oare ace w t anincrediblycomplexsystemthat,forallpractical

purposes,exists

independently

of

them.

(pp.149

150)

AlbertLszl Barabsi,Linked,PlumePubl.2003.

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Social Computing - Intro