Crowdsourcing Meetup at Stanford May 3, 2011

1. Crowdsourcing for Human Computer
Interaction Research
Ed H. Chi
Research Scientist
Google
(work done while at [Xerox] PARC with Aniket Kittur)

2. User studies
• Getting input from users is important in HCI
– surveys
– rapid prototyping
– usability tests
– cognitive walkthroughs
– performance measures
– quantitative ratings

3. User studies
• Getting input from users is expensive
– Time costs
– Monetary costs
• Often have to trade off costs with sample size

4. Online solutions
• Online user surveys
• Remote usability testing
• Online experiments
• But still have difficulties
– Rely on practitioner for recruiting participants
– Limited pool of participants

5. Crowdsourcing
• Make tasks available for anyone online to complete
• Quickly access a large user pool, collect data, and
compensate users
• Example: NASA Clickworkers
– 100k+ volunteers identified Mars craters from
space photographs
– Aggregate results virtually indistinguishable from
expert geologists
experts
crowds
http://clickworkers.arc.nasa.gov

6. Amazon s Mechanical turk
• Market for human intelligence tasks
• Typically short, objective tasks
– Tag an image
– Find a webpage
– Evaluate relevance of search results
• Users complete for a few pennies each

7. Example task

8. Using Mechanical Turk for user studies
Traditional user Mechanical Turk
studies
Task complexity Complex Simple
Long Short
Task subjectivity Subjective Objective
Opinions Verifiable
User information Targeted demographics Unknown demographics
High interactivity Limited interactivity
Can Mechanical Turk be usefully used for user studies?

9. Task
• Assess quality of Wikipedia articles
• Started with ratings from expert Wikipedians
– 14 articles (e.g., Germany , Noam Chomsky )
– 7-point scale
• Can we get matching ratings with mechanical turk?

10. Experiment 1
• Rate articles on 7-point scales:
– Well written
– Factually accurate
– Overall quality
• Free-text input:
– What improvements does the article need?
• Paid $0.05 each

11. Experiment 1: Good news
• 58 users made 210 ratings (15 per article)
– $10.50 total
• Fast results
– 44% within a day, 100% within two days
– Many completed within minutes

12. Experiment 1: Bad news
• Correlation between turkers and Wikipedians
only marginally significant (r=.50, p=.07)
• Worse, 59% potentially invalid responses
Experiment 1
Invalid 49%
comments
<1 min 31%
responses
• Nearly 75% of these done by only 8 users

13. Not a good start
• Summary of Experiment 1:
– Only marginal correlation with experts.
– Heavy gaming of the system by a minority
• Possible Response:
– Can make sure these gamers are not rewarded
– Ban them from doing your hits in the future
– Create a reputation system [Delores Lab]
• Can we change how we collect user input ?

14. Design changes
• Use verifiable questions to signal monitoring
– How many sections does the article have?
– How many images does the article have?
– How many references does the article have?

15. Design changes
• Use verifiable questions to signal monitoring
• Make malicious answers as high cost as
good-faith answers
– Provide 4-6 keywords that would give someone a
good summary of the contents of the article

16. Design changes
• Use verifiable questions to signal monitoring
• Make malicious answers as high cost as
good-faith answers
• Make verifiable answers useful for completing
task
– Used tasks similar to how Wikipedians described
evaluating quality (organization, presentation,
references)

17. Design changes
• Use verifiable questions to signal monitoring
• Make malicious answers as high cost as
good-faith answers
• Make verifiable answers useful for completing
task
• Put verifiable tasks before subjective
responses
– First do objective tasks and summarization
– Only then evaluate subjective quality
– Ecological validity?

18. Experiment 2: Results
• 124 users provided 277 ratings (~20 per article)
• Significant positive correlation with Wikipedians (r=.
66, p=.01)
• Smaller proportion malicious responses
• Increased time on task
Experiment 1 Experiment 2
Invalid 49% 3%
comments
<1 min 31% 7%
responses
Median time 1:30 4:06

19. Generalizing to other user studies
• Combine objective and subjective questions
– Rapid prototyping: ask verifiable questions about
content/design of prototype before subjective
evaluation
– User surveys: ask common-knowledge questions
before asking for opinions

20. Limitations of mechanical turk
• No control of users environment
– Potential for different browsers, physical
distractions
– General problem with online experimentation
• Not designed for user studies
– Difficult to do between-subjects design
– Involves some programming
• Users
– Uncertainty about user demographics, expertise

21. Quick Summary
• Mechanical Turk offers the practitioner a way to
access a large user pool and quickly collect data at
low cost
• Good results require careful task design
1. Use verifiable questions to signal monitoring
2. Make malicious answers as high cost as good-faith
answers
3. Make verifiable answers useful for completing task
4. Put verifiable tasks before subjective responses

22. Crowdsourcing for HCI Research
• Does my interface/visualization work?
– WikiDashboard: transparency visualization for Wikipedia
– J. Heer’s work at Stanford at looking at perceptual effects
• Coding of large amount of user data
– What is a question? In Twitter, Sharoda Paul at PARC
• Decompose tasks into smaller tasks
– Digital Taylorism
– Frederick Winslow Taylor (1856-1915) 1911 book
'Principles Of Scientific Management'
• Incentive mechanisms
– Intrinsic vs. Extrinsic rewards
– Games vs. Pay

23. • @edchi
• chi@acm.org
• http://edchi.net

24. What would make you trust Wikipedia more?
24

25. What is Wikipedia?
Wikipedia is the best thing ever. Anyone in the world can write
anything they want about any subject, so you know you re getting the
best possible information.
– Steve Carell, The Office
25

26. What would make you trust Wikipedia more?
Nothing
26

27. What would make you trust Wikipedia more?
Wikipedia, just by its nature, is
impossible to trust completely. I don't
think this can necessarily be
changed.
27

28. WikiDashboard
 Transparency of social dynamics can reduce conflict and coordination
issues
 Attribution encourages contribution
– WikiDashboard: Social dashboard for wikis
– Prototype system: http://wikidashboard.parc.com
 Visualization for every wiki page
showing edit history timeline and
top individual editors
 Can drill down into activity history
for specific editors and view edits
to see changes side-by-side
Citation: Suh et al.
CHI 2008 Proceedings
Crowdsourcing Meetup (Stanford 28

29. Hillary
Clinton
Crowdsourcing Meetup (Stanford 29
2011) 29

30. Top
Editor
-­‐
Wasted
Time
R
Crowdsourcing Meetup (Stanford 30
2011)

31. Surfacing information
• Numerous studies mining Wikipedia revision
history to surface trust-relevant information
– Adler & Alfaro, 2007; Dondio et al., 2006; Kittur et al., 2007;
Viegas et al., 2004; Zeng et al., 2006
Suh, Chi, Kittur, & Pendleton, CHI2008
• But how much impact can this have on user
perceptions in a system which is inherently
mutable?
31

32. Hypotheses
1. Visualization will impact perceptions of trust
2. Compared to baseline, visualization will
impact trust both positively and negatively
3. Visualization should have most impact when
high uncertainty about article
• Low quality
• High controversy
32

33. Design
• 3 x 2 x 2 design
Controversial Uncontroversial
Visualization Abortion Volcano
High quality
• High stability George Bush Shark
• Low stability
• Baseline (none) Pro-life feminism Disk
defragmenter Low quality
Scientology and
celebrities Beeswax
33

34. Example: High trust visualization
34

35. Example: Low trust visualization
35

36. Summary info
• % from anonymous
users
36

37. Summary info
• % from anonymous
users
• Last change by
anonymous or
established user
37

38. Summary info
• % from anonymous
users
• Last change by
anonymous or
established user
• Stability of words
38

39. Graph
• Instability
39

40. Method
• Users recruited via Amazon s Mechanical Turk
– 253 participants
– 673 ratings
– 7 cents per rating
– Kittur, Chi, & Suh, CHI 2008: Crowdsourcing user studies
• To ensure salience and valid answers, participants
answered:
– In what time period was this article the least stable?
– How stable has this article been for the last month?
– Who was the last editor?
– How trustworthy do you consider the above editor?
40

41. Results
7 High stability Baseline Low stability
6
Trustworthiness rating
5
4
3
2
1
Low qual High qual Low qual High qual
Uncontroversial Controversial
main effects of quality and controversy:
• high-quality articles > low-quality articles (F(1, 425) = 25.37, p < .001)
• uncontroversial articles > controversial articles (F(1, 425) = 4.69, p = .
031)
41

42. Results
7 High stability Baseline Low stability
6
Trustworthiness rating
5
4
3
2
1
Low qual High qual Low qual High qual
Uncontroversial Controversial
interaction effects of quality and controversy:
• high quality articles were rated equally trustworthy whether controversial
or not, while
• low quality articles were rated lower when they were controversial than
when they were uncontroversial.
42

43. Results
1. Significant effect of 7 High stability Baseline Low stability
visualization 6
Trustworthiness rating
– High > low, p < .001 5
2. Viz has both positive and 4
negative effects 3
– High > baseline, p < .001 2
– Low > baseline, p < .01 1
Low qual High qual Low qual High qual
3. No interaction of Uncontroversial Controversial
visualization with either
quality or controversy
– Robust across conditions
43

44. Results
1. Significant effect of 7 High stability Baseline Low stability
visualization 6
Trustworthiness rating
– High > low, p < .001 5
2. Viz has both positive and 4
negative effects 3
– High > baseline, p < .001 2
– Low > baseline, p < .01 1
Low qual High qual Low qual High qual
3. No interaction of Uncontroversial Controversial
visualization with either
quality or controversy
– Robust across conditions
44

45. Results
1. Significant effect of 7 High stability Baseline Low stability
visualization 6
Trustworthiness rating
– High > low, p < .001 5
2. Viz has both positive and 4
negative effects 3
– High > baseline, p < .001 2
– Low > baseline, p < .01 1
Low qual High qual Low qual High qual
3. No interaction effect of Uncontroversial Controversial
visualization with either
quality or controversy
– Robust across conditions
45