CROWDSOURCING AND ITS APPLICATIONS ON SCIENTIFIC RESEARCH

Crowdsourcing= Crowd + Outsourcing

“soliciting solutions via open calls to large-scale

communities”

Some Examples

Call for professional helps Award 50,000 to 1,000,000 for each tasks

Office work platform

Microtask platform Over 30,000 tasks at the same time

What Tasks are crowdsourceable?

Software Development

Reward: 25,000 USD

Data Entry

Reward: 4.4 USD/hour

Image Tagging

Reward: 0.04 USD

Trip Advice

Reward: points on Yahoo! Answers

The impact of crowdsourcing on scientific research?

Amazon Mechanical Turk

A micro-task marketplace Task prices are usually between 0.01

to 1 USD Easy-to-use interface

Amazon Mechanical Turk

Human Intelligence Task (HIT) Tasks hard for computers

Developer Prepay the money Publish HITs Get results

Worker Complete the HITs Get paid

Who are the workers?

A Survey of Mechanical Turk Survey on 1000 Turkers (Turk

workers) Two identical surveys (Oct. 2008 and

Dec. 2008) Consistent results Blog post:

A Computer Scientist in a Business School

美國77%

印度5%

菲律賓3%

加拿大3%

英國2%

德國1%

義大利0%

其他9%

來源國家

Education

AgeGender

Annual Income

Compare with Internet Demographics Use the data from ComScore

In summary, Tukers are younger

Portion of 21-35 years old: 51% vs. 22% in internet mainly female

70% female vs. 50 % female having lower income

65% turkers with income < 60k/year vs. 45% in internet

having smaller family 55% turkers have no children vs. 40% in internet

How Much Turkers Earn?

Why Turkers Turk?

Research Applications

Dataset Collection

Dataset is important in computer science!

In multimedia analysis Is there X in the image Where is Y in the image

In natural language processing What is the emotion of this sentence

And in lots of other applications

Dataset Collection

Utility Annotation By Sorokin and Forsyth at UIUC Image analysis

Type keyword Select examples Click on landmarks Outline figures

0.01 USD/ task

0.02 USD/ task

0.01 USD/ task

0.01 USD/ task

Dataset Collection

Linguistic annotations (Snow et al. 2008) Word similarity

USD 0.2 to label 30 word pairs

Dataset Collection

Linguistic annotations (Snow et al. 2008) Affect recognition

USD 0.4 to label 20 headlines (140 labels)

Dataset Collection

Linguistic annotations (Snow et al. 2008) Textual entailment

If “Microsoft was established in Italy in 1985”, then “Microsoft was established in 1985” ?

Word sense disambiguation “a bass on the line” vs. “a funky bass line”

Temporal annotation Ran happens before fell:

“The horse ran past the barn fekk”

Dataset Collection

Document relevance evaluation Alonso et al. (2008)

User rating collection Kittur et al. (2008)

Noun compound paraphrasing Nakov (2008)

Name resoluation Su et al. (2007)

Data Characteristic

Cost? Efficiency? Quality?

Cost and Efficiency

In image annotation Sorokin and Forsyth, 2008

Cost and Efficiency

In linguistic annotation Snow et. al, 2008

Cheap and fast!

Is it good?

Quality

Multiple non-experts can beat experts 三個臭皮匠勝過一個諸葛亮 Black line

agreement among turkers

Green line: single expert

Golden result: agreement among

multiple experts

In addition toDataset Collection

QoE Measurement

QoE (Quality of Experience) Subjective measure of user perception

Traditional approach User studies by MOS ratings (Bad ->

Excellent) Crowdsourcing with paired

comparison Diverse user input Easy to understand Interval scale scores can be calculated

Acoustic QoE Evaluation

Acoustic QoE Evaluation

Which one is better? Simple pair comparison

Optical QoE evaluation

Interactive QoE Evaluation

Acoustic QoE

MP3 Compression Rate

VoIP Loss Rate

Optical QoE

Video Codec Packet loss rate

Iterative Task

Iterative Tasks

Turkit: tools for iterative tasks on Mturk

Imperative programming paradigm Basic elements

Variable (a = b) Control (if else statement) Loop (for, while statement)

Turning MTurk into a programming platform which integrates human brain powers

Iterative Text Improvement A Wikipedia-like scenario

One Turker improve the text Other Turkers vote if the improvement is

valid

Iterative Text Improvement Image description

Instructions for the improve-HIT Please improve the description for this image People will vote whether to approve your

changes Use no more than 500 characters

Instructions for the vote-HIT Please select the better description for this

image Your vote must agree with the majority to be

approved

Iterative Text Improvement Image description A partial view of a pocket calculator

together with some coins and a pen.

A view of personal items a calculator, and some gold and copper coins, and a round tip pen, these are all pocket and wallet sized item used for business, writing, calculating prices or solving math problems and purchasing items.

A close-up photograph of the following items:* A CASIO multi-function calculator* A ball point pen, uncapped* Various coins, apparently European, both copper and gold

…Various British coins; two of £1 value, three of 20p value and one of 1p value. …

Iterative Text Improvement Image descriptionA close-up photograph of the following

items:

A CASIO multi-function, solar powered scientific calculator.

A blue ball point pen with a blue rubber grip and the tip extended.

Six British coins; two of £1 value, three of 20p value and one of 1p value.

Seems to be a theme illustration for a brochure or document cover treating finance - probably personal finance.

Iterative Text Improvement Handwriting Recognition

Version 1 You (?) (?) (?) (work). (?) (?) (?) work (not)

(time). I (?) (?) a few grammatical mistakes. Overall your writing style is a bit too (phoney). You do (?) have good (points), but they got lost amidst the (writing). (signature)

Iterative Text Improvement Handwriting Recognition

Version 6 “You (misspelled) (several) (words). Please spell-

check your work next time. I also notice a few grammatical mistakes. Overall your writing style is a bit too phoney. You do make some good (points), but they got lost amidst the (writing). (signature)”

Cost and Efficiency

More on Methodology

Repeated Labeling

Crowdsourcing -> Multiple imperfect labeler Each worker is a labeler Labels are not always correct

Repeated labeling Improve the supervised induction

Increase the single-label accuracy Decrease the cost for acquiring training data

Repeated Labeling

Repeated labeling helps improve the overall quality when the accuracy of single labeler low.

Selected Repeated Labeling Repeat-label the most uncertain points

Label uncertainty (LU) Whether the label distribution is stable Calculated from beta distribution

Model uncertainty (MU) Whether the model has high confidence

for the label Calculated from model predictions

Selected Repeated Labeling Selected repeated labeling improves

the overall quality of crowdsourcing approach.

GRR: no selected repeated labelingMU: Model UncertaintyLU: Label UncertaintyLMU: integrate Label and Model Uncertainty

Incentive vs. Performance

High financial incentive -> high performance?

User studies (Mason and Watt 2009) Order images

Ex: choose the busiest image

Solve word puzzles

Incentive vs. Performance

High incentive -> high quantity, not high quality

Incentive vs. Performance

Workers always wants moreH

ow

much

work

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thin

k th

ey d

ese

rve

Users would be influenced by their paid amount

Pay little at first, and incrementally increase the payment

Conclusion

Crowdsourcing provides a new paradigm and a new platform for computer science researches.

New applications, new methodologies, and new businesses are quickly developing with the aid of crowdsouring.