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SOCIALinterface
smax van kleek
sociam retreat
pony land, new forest
1 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 methods
convenience sampling, snowball sampling
statistical data analysis,
multi-factor latent process analysis
robust statistical ranking, scoring
game-proo鍖ng 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
registers
1.
2.A question/tournament
is chosen
One of three types of
rounds are selected randomly
3.
The users score is tallied
and their rankings are
displayed. Next round starts.4.
Twiage - Van Kleek et al. CHI 2012
Tuesday, 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
Agreementmeasures
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
roundtype
rank 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, .Crowd鍖ltering
Tuesday, 7 May 13
Yahoo! Answers, Knowledge NI, QuoraSocial Q&A
Tuesday, 7 May 13
Software development + debugging assistancSocial Coding
Tuesday, 7 May 13
Software development + debugging assistancSocial Coding
Tuesday, 7 May 13
Tumblr - Pinterest/Pinspire - Path
76.6 Million Blogs, 32.6 Billion Posts
50GB of posts added油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 motivation
instinct
incentive
drive
arousal
identity/self-expression
role ful鍖llment
brand/image/community
association
social contact
Tuesday, 7 May 13
theories of human motivation
instinct
incentive
drive
arousal
identity/self-expression
role ful鍖llment
brand/image/community
association
social contact
what does this have to do with interface
design?
{ fb / G+ } - real-name policy
the saga of orkut, myspace and dead
communities
the 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
Luces Model of Choice
Violations of principle of proportionality
Similarity Hypothesis (Tversky et al. 1972)
Regularity Hypothesis
Asymmetrically Dominated Alternatives
(Huber et al. 1982)
Tuesday, 7 May 13
Tuesday, 7 May 13
SNACKBOT
Min Kyung Lee, CMU CHI
2011
Tuesday, 7 May 13
default choice bias
present-biased preferences
asymmetric choice
SNACKBOT
Min 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
SNACKBOT
Min 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 today
would 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
SNACKBOT
Min 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 today
would 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
apple - celery - cookie
SNACKBOT
Min 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 interfaces
can borrow/beg/steal from social and cognitive science
Crowd-data distillation
Group coordination and mobilisation
Core motivation
Lightweight structures of
distributed management and coordination
Interface design choices that compel humans
to be empowered, participate, motivate.
Tuesday, 7 May 13

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Social Interfaces

  • 1. SOCIALinterface smax van kleek sociam retreat pony land, new forest 1 october 2012 Tuesday, 7 May 13
  • 2. 3 challenges 1 - crowdsourcing data 2 - human motivation 3 - behavioural design Tuesday, 7 May 13
  • 3. 1 - crowdsourcing data 2 - human motivation 3 - behavioural design 3 challenges Tuesday, 7 May 13
  • 6. mumbai terror crisis motivation is clear; Tuesday, 7 May 13
  • 7. mumbai terror crisis haiti damage report motivation is clear; Tuesday, 7 May 13
  • 8. mumbai terror crisis haiti damage report japan nuclear crisis motivation is clear; Tuesday, 7 May 13
  • 9. mumbai terror crisis haiti damage report japan nuclear crisis motivation is clear; data challenges remain. Tuesday, 7 May 13
  • 10. C1. violations of statistical assumptions C2. variability in expertise: unknown experience, biases motivation and intention factors C3. gaming the system Tuesday, 7 May 13
  • 11. C1. violations of statistical assumptions C2. variability in expertise: unknown experience, biases motivation and intention factors C3. gaming the system non-probability sampling methods convenience sampling, snowball sampling statistical data analysis, multi-factor latent process analysis robust statistical ranking, scoring game-proo鍖ng design Tuesday, 7 May 13
  • 12. [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
  • 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
  • 14. likert ordering pickbest User visits http://twiage.me and registers 1. 2.A question/tournament is chosen One of three types of rounds are selected randomly 3. The users score is tallied and their rankings are displayed. Next round starts.4. Twiage - Van Kleek et al. CHI 2012 Tuesday, 7 May 13
  • 15. 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 Agreementmeasures randomly divide Tuesday, 7 May 13
  • 16. 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 roundtype rank agreement Tuesday, 7 May 13
  • 17. 1 - crowdsourcing data 2 - human motivation 3 - behavioural design 3 challenges Tuesday, 7 May 13
  • 21. Reddit, Digg, Slashdot, HackerNews, .Crowd鍖ltering Tuesday, 7 May 13
  • 22. Yahoo! Answers, Knowledge NI, QuoraSocial Q&A Tuesday, 7 May 13
  • 23. Software development + debugging assistancSocial Coding Tuesday, 7 May 13
  • 24. Software development + debugging assistancSocial Coding Tuesday, 7 May 13
  • 25. Tumblr - Pinterest/Pinspire - Path 76.6 Million Blogs, 32.6 Billion Posts 50GB of posts added油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
  • 26. what attracts participation in these communities? what drives continued involvement? what causes communities and platforms to fail ? Tuesday, 7 May 13
  • 27. theories of human motivation instinct incentive drive arousal identity/self-expression role ful鍖llment brand/image/community association social contact Tuesday, 7 May 13
  • 28. theories of human motivation instinct incentive drive arousal identity/self-expression role ful鍖llment brand/image/community association social contact what does this have to do with interface design? { fb / G+ } - real-name policy the saga of orkut, myspace and dead communities the un-instagrams Tuesday, 7 May 13
  • 29. 1 - people as data sources 2 - human motivation 3 - behavioural design 3 challenges Tuesday, 7 May 13
  • 30. HOW DO interface ARTEFACTS affect human behaviour? Tuesday, 7 May 13
  • 31. Luces Model of Choice Violations of principle of proportionality Similarity Hypothesis (Tversky et al. 1972) Regularity Hypothesis Asymmetrically Dominated Alternatives (Huber et al. 1982) Tuesday, 7 May 13
  • 33. SNACKBOT Min Kyung Lee, CMU CHI 2011 Tuesday, 7 May 13
  • 34. default choice bias present-biased preferences asymmetric choice SNACKBOT Min Kyung Lee, CMU CHI 2011 Tuesday, 7 May 13
  • 35. tomorrow you would like : salad - sandwich for a snack: apple - cookie default choice bias present-biased preferences asymmetric choice SNACKBOT Min Kyung Lee, CMU CHI 2011 Tuesday, 7 May 13
  • 36. tomorrow you would like : salad - sandwich for a snack: apple - cookie You selected a salad for lunch today would 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 SNACKBOT Min Kyung Lee, CMU CHI 2011 Tuesday, 7 May 13
  • 37. tomorrow you would like : salad - sandwich for a snack: apple - cookie You selected a salad for lunch today would 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 apple - celery - cookie SNACKBOT Min Kyung Lee, CMU CHI 2011 Tuesday, 7 May 13
  • 38. summary Rapid Assembly of Social Machines FLOSS Software Platforms (Tech. Infrastructure) To make truly effective Social Machines social interfaces can borrow/beg/steal from social and cognitive science Crowd-data distillation Group coordination and mobilisation Core motivation Lightweight structures of distributed management and coordination Interface design choices that compel humans to be empowered, participate, motivate. Tuesday, 7 May 13