SOCIALinterfaces
max van kleeksociam retreat
pony land, new forest1 october 2012
Tuesday, 7 May 13
3 challenges
1 - crowdsourcing data
2 - human motivation
3 - behavioural design
Tuesday, 7 May 13
1 - crowdsourcing data
2 - human motivation
3 - behavioural design
3 challenges
Tuesday, 7 May 13
Tuesday, 7 May 13
motivation is clear;
Tuesday, 7 May 13
mumbai terror crisis
motivation is clear;
Tuesday, 7 May 13
mumbai terror crisis
haiti damage report
motivation is clear;
Tuesday, 7 May 13
mumbai terror crisis
haiti damage report japan nuclear crisis
motivation is clear;
Tuesday, 7 May 13
mumbai terror crisis
haiti damage report japan nuclear crisis
motivation is clear;
data challenges remain.
Tuesday, 7 May 13
C1. violations of statistical assumptions
C2. variability in expertise: unknown experience, biases motivation and intention factors
C3. gaming the system
Tuesday, 7 May 13
C1. violations of statistical assumptions
C2. variability in expertise: unknown experience, biases motivation and intention factors
C3. gaming the system
non-probability sampling methodsconvenience sampling, snowball sampling
statistical data analysis, multi-factor latent process analysisrobust statistical ranking, scoring
game-proofing design
Tuesday, 7 May 13
[Haiti Earthquake Usahidi] Can unbounded crowdsourcing (non-representative sampling) via SMS predict actual damage? http://irevolution.net/2010/10/13/crowdsourced-prediction/
Tuesday, 7 May 13
8,163 copies of War and Peace(June 2011)
“Is It Really About Me? Message Content in Social Awareness Streams” [Naaman et al. CSCW ’10]
information sharing (IS) 0.2
self promotion (SP) 0.05
opinions/complaints (OC) 0.25
statements and random thoughts 0.25
me now (ME) 0.4
questions to followers (QF) 0.05
presence maintenance (PM) 0.05
anecdote by me (AM) 0.05
anecdote by others (AO) 0.01
Tuesday, 7 May 13
likert
ordering
pickbest
User visits http://twiage.me and registers1.
2.A question/tournament is chosen
One of three types ofrounds are selected randomly3.
The user’s score is talliedand their rankings are displayed. Next round starts.4.
Twiage - Van Kleek et al. CHI 2012Tuesday, 7 May 13
Q1:
Q2:
Q3:
twiage
user selections/ratings
EWR ELO LR
rank set (66%)
Rank agreement
Classification agreement (Top 5%)
test set (33%)
EWR ELO LR
Agre
emen
t mea
sure
s
randomly divide
Tuesday, 7 May 13
Empirical Win Elo Raw Likert
All 69.7 (0.9) 72.8 (0.9) n/a
Pick-Best 71.2 (3.0) 75.5 (0.8) n/a
Likert 67.7 (1.6) 71.4 (1.6) 64.8 (2.0)
Ordering 63.3 (1.9) 62.4 (2.2) n/a
ranking scheme
roun
d ty
perank agreement
Tuesday, 7 May 13
1 - crowdsourcing data
2 - human motivation
3 - behavioural design
3 challenges
Tuesday, 7 May 13
Tuesday, 7 May 13
minecraft enterprise-d
Tuesday, 7 May 13
Tuesday, 7 May 13
Reddit, Digg, Slashdot, HackerNews, ... CrowdfilteringTuesday, 7 May 13
Yahoo! Answers, Knowledge NI, QuoraSocial Q&ATuesday, 7 May 13
Software development + debugging assistanceSocial CodingTuesday, 7 May 13
Software development + debugging assistanceSocial CodingTuesday, 7 May 13
Tumblr - Pinterest/Pinspire - Path76.6 Million Blogs, 32.6 Billion Posts50GB of posts added each day; follower list updates are roughly another 2.7 terabytes daily
Youtube, Vimeo
Content creation and curation networks:
Blogging, Tumblogging, Vlogging, Geotumbling, Facebooking.
Wordpress, Livejournal,
Tuesday, 7 May 13
what attracts participation in these communities?
what drives continued involvement?
what causes communities and platforms to fail ?
Tuesday, 7 May 13
theories of human motivationinstinctincentivedrivearousal
identity/self-expressionrole fulfillmentbrand/image/community associationsocial contact
Tuesday, 7 May 13
theories of human motivationinstinctincentivedrivearousal
identity/self-expressionrole fulfillmentbrand/image/community associationsocial contact
what does this have to do with interface design?
{ fb / G+ } - real-name policythe saga of orkut, myspace and dead communitiesthe un-instagrams
Tuesday, 7 May 13
1 - people as data sources
2 - human motivation
3 - behavioural design
3 challenges
Tuesday, 7 May 13
HOW DO interface ARTEFACTS affect human behaviour?
Tuesday, 7 May 13
Luce’s Model of Choice
Violations of principle of proportionality
Similarity Hypothesis (Tversky et al. 1972)Regularity HypothesisAsymmetrically Dominated Alternatives (Huber et al. 1982)
Tuesday, 7 May 13
Tuesday, 7 May 13
SNACKBOTMin Kyung Lee, CMU CHI 2011
Tuesday, 7 May 13
default choice bias
present-biased preferences
asymmetric choice
SNACKBOTMin Kyung Lee, CMU CHI 2011
Tuesday, 7 May 13
tomorrow you would like : salad - sandwich
for a snack: apple - cookie
default choice bias
present-biased preferences
asymmetric choice
SNACKBOTMin Kyung Lee, CMU CHI 2011
Tuesday, 7 May 13
tomorrow you would like : salad - sandwich
for a snack: apple - cookie
You selected a salad for lunch todaywould you like to keep this choice
or switch to a Roast Beef Sandwich
what would you like tomorrow?
default choice bias
present-biased preferences
asymmetric choice
SNACKBOTMin Kyung Lee, CMU CHI 2011
Tuesday, 7 May 13
tomorrow you would like : salad - sandwich
for a snack: apple - cookie
You selected a salad for lunch todaywould you like to keep this choice
or switch to a Roast Beef Sandwich
what would you like tomorrow?
default choice bias
present-biased preferences
asymmetric choiceapple - celery - cookie
SNACKBOTMin Kyung Lee, CMU CHI 2011
Tuesday, 7 May 13
summary“Rapid Assembly of Social Machines”
FLOSS Software Platforms (Tech. Infrastructure)
To make truly effective Social Machines social interfacescan borrow/beg/steal from social and cognitive science
Crowd-data distillationGroup coordination and mobilisationCore motivation Lightweight structures of distributed management and coordinationInterface design choices that compel humans to be empowered, participate, motivate.
Tuesday, 7 May 13
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