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
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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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