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