Expertise Sharing Dynamics in Online Forums - Lada Adamic · Sharing Dynamics in Online Forums motivation related work Java Forum: inferring expertise Yahoo Answers Clustering categories

ExpertiseSharing

Dynamicsin OnlineForums

motivation

related work

Java Forum:inferringexpertise

YahooAnswersClustering categories

Characterizingbreadth of users

Predicting bestanswers

Witkeys:competing toshareexpertise

Conclusion

Expertise Sharing Dynamicsin Online Forums

Lada Adamic

w/ Jun Zhang, Mark Ackerman,Eytan Bakshy, Jiang Yang

School of Information,University of Michigan

CMU machine learning/Google seminar 5/5/2008

ExpertiseSharing


motivation

related work






Conclusion

Outline

1 motivation

2 related work

3 Java Forum: inferring expertise

4 Yahoo AnswersClustering categoriesCharacterizing breadth of usersPredicting best answers

5 Witkeys: competing to share expertise

6 Conclusion

ExpertiseSharing


motivation

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Conclusion

motivation: millions of users are using the Webto pose and answer questions

Knows Knowledge iN

ExpertiseSharing


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Conclusion

oozing out knowledge

“Knowledge search is like oozing out knowledge inhuman brains to the Internet. People who knowsomething better than others can present theirknow-how, skills or knowledge"

NHN CEO Chae Hwi-young

Knowledge In

ExpertiseSharing


motivation

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Conclusion

oozing out knowledge

“(It is) the next generation of search. . . (it) is a kindof collective brain – a searchable database ofeverything everyone knows. It’s a culture ofgenerosity. The fundamental belief is thateveryone knows something."

Eckart Walther (Yahoo! Research)

ExpertiseSharing


motivation

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Conclusion

Limitations of current systems

4939N =

ExpertiseRating

lowhigh

WAI

TTIM

E(m

in)

10000

9000

8000

7000

6000

5000

4000

3000

2000

1000

0

6996

41

Automatically inferring expertise could be helpfulresponse time gapexpertise gapdifficult to infer reliability of answers

ExpertiseSharing


motivation

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Conclusion

Outline

1 motivation

2 related work




6 Conclusion

ExpertiseSharing


motivation

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Conclusion

related work: study of online communities

NetScan (Smith, Fisher, et al.)“Answer People"Motivations in online participation(Lakhani & Hippel, Kraut)Expertise recommenders

ContactFinder (Krulwich et al.),Answer Garden (Ackerman)Small Blue (Lin)

Automatic evaluation of expertiselevels

Using different text resources(Kautz, et al, and a lot of others)Using email networks (Campbell etal.)

ExpertiseSharing


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Conclusion

related work: QA sites

Harper et al. CHI 2008: field analysis of QA sitespaying for (Google) Answers lead to higher answerquality than not (Yahoo! Answers).but free (open) QA sites outperform sites with dedicatedexperts

Agichtein et al. CIKM2007, WSDM 2008: Identifyinggood answers

use textual analysis, clicks, and community ratingsfind that good questions lead to good answers

Gyöngyi et al. (QA Workshop @ WWW2008)Questioning Yahoo! Answers

using HITS to identify good contributors:good askers attract the attention of good repliers

ExpertiseSharing


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Conclusion

Our work on expertise sharing

Zhang et al. WWW2007, C&T2007: ExpertiseRankanalyze Sun’s Java Forumuse link analysis to identify expertssimulate underlying dynamics

Zhang et al. UIST 2007modify forum interface to match expertise

Adamic et al. WWW 2008broad set of categoriesdoes focus matter?

Yang et al. ICWSM 2008, EC 2008users compete to provide best answer for $$infer task and user prestige from interactions

ExpertiseSharing


motivation

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Conclusion

Outline

1 motivation

2 related work




6 Conclusion

ExpertiseSharing


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Conclusion

Sun’s Java Forum

87 sub-forums1,438,053 messagescommunity expertisenetwork constructed

196,191 users796,270 edges

ExpertiseSharing


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Conclusion

Constructing an expertise network

A B C

Thread 1 Thread 2

Thread 1: Large Data, binary search or hashtable? user A Re: Large... user B Re: Large... user C Thread 2: Binary file with ASCII data user A Re: File with... user C

A

B

C

1

1

A

B

C

1

2

A

B

C

1/2

1+1//2

A

B

C

0.9 0.1

unweighted

weighted by # threads

weighted by shared credit

weighted with backflow

ExpertiseSharing


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Conclusion

Sun’s Java Forum

‘answer people’may reply tothousands ofothers’questionpeople’ mayelicit manyresponses

10 0 10 1 10 2 10 3 10 -4

10 -3

10 -2

10 -1

10 0

degree (k)

cum

ulat

ive p

roba

bility

α = 1.87 fit, R 2 = 0.9730

number of people one received replies from

number of people one replied to

ExpertiseSharing


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Conclusion

Not everyone asks/replies

The Java Forum network is an uneven bow tie

The Web is a bow tie

IN many askersOUT people who usually only answerSCC generalized reciprocity core

ExpertiseSharing


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Conclusion

Fragment of the Java Forum

ExpertiseSharing


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Conclusion

Relating network structure to expertise

Human-rated expertise levels2 raters135 JavaForum users with >= 10 postsinter-rater agreement (t = 0.74, r = 0.83)for evaluation of algorithms, omit users where ratersdisagreed by more than 1 level (t = 0.80, r = 0.83)

L Category Description5 Top Java expert Knows the core Java theory and advanced topics.4 Java professional Can answer all or most of Java concept questions.l3 Java user Knows advanced Java concepts. .2 Java learner Knows basic concepts and can program.1 Newbie Just starting to learn java.

ExpertiseSharing


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Conclusion

Automated vs. human ratings

Top K Kendall’s τ Spearman’s ρ

# answers z-score # answers indegree z-score indegree PageRank HITS authority

0.9 0.8 0.7 0.6

0.5 0.4 0.3 0.2 0.1

0

All measures give good agreement.Some simple (non-network) measures work best.

ExpertiseSharing


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Conclusion

Modeling community dynamics to explainalgorithm performance

ExpertiseNet Simulator Control Parameters:

Distribution ofexpertise

Who asksquestions mostoften?

Who answersquestions mostoften?

best expertmost likelysomeone abit moreexpert

ExpertiseSharing


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Conclusion

Models of expertise pairing

0 1 2 3 4 50

1

2

3

4

5

replier expertise

asker expertise

0

0.05

0.1

0.15

‘best’ preferred ‘just better’ preferred

iep ijij /~ )( −β iep ji

ij /~ )( −γ j>i

0 1 2 3 4 50

1

2

3

4

5

replier expertise

asker expertise

0.02

0.04

0.06

0.08

0.1

0.12

pij = probability a user of expertise j replies to user ofexpertise i

ExpertiseSharing


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Conclusion

Resulting networks

Best “preferred” just better

ExpertiseSharing


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Conclusion

Degree correlation profiles

Java Forum Network

best preferred (simulation) just better (simulation)

ExpertiseSharing


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Conclusion

Simulation can aid in algorithm section

Preferred Helper: ‘best available’

Preferred Helper: ‘just better’

ExpertiseSharing


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Conclusion

Summary of Java Forum findings

Expertise Networks have interesting characteristicsA set of useful metricsRanking algorithms are affected by network structuresSimulation as an analysis toolThere are rich design opportunities

Find experts with the help of structural information (andcontent analysis)Predict good answersRe-order questions/answers to match expertise

UIST2007: “Expertise-Level based Interface Personalization for Online Help-seeking Communities”

ExpertiseSharing


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Conclusion

Outline

1 motivation

2 related work




6 Conclusion

ExpertiseSharing


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Conclusion

What is being shared?

Not everyone is a Java expert, but everyone knowssomething...

cars & transportation

maintenance & repairs

beauty & style

hair

ExpertiseSharing


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Conclusion

Why not just search the web?

can’t spell what does this Russian phrase mean? "dobroeutro not horoshee"

common-sense knowledge What will happen if I leave theracks in my gas oven while using the cleaningcycle?

support Q: How do I get rid of my fear of bees?A1: Being afraid of bees is a pretty reasonablefear...A2: I’m like that too!

discussion Have conservatives been good for the USA?

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Conclusion

Data we collected

1 month (Feb. 2007)

8,452,337 answers

1,178,983 questions

unique repliers: 433,402

unique askers: 495,414

users who are both askersand helpers: 211,372

ExpertiseSharing


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Conclusion

category popularity and intensity of response

ExpertiseSharing


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Conclusion

category: baby names

ExpertiseSharing


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Conclusion

category: alternative science

ExpertiseSharing


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Conclusion

Are the replies numerous? Lengthy?

200 300 400 500 600 700 800

05

1015

2025

30

post length

thre

ad le

ngth

Physics

ParentingPolls

WrestlingDating

Repairs CancerCelebrities

ProgrammingHistory

Music

ReligionMarriage

Hair

WeddingsJokes

Baby Names

Photography

Cats Dogs

Genealogy

Politics

CleaningImmigration

Horoscopes

Y! Groups

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Conclusion

Are the same users asker and repliers?

Let aiA, and riA be the number of answers and replies,respectively by user i in category A.

asker/replier overlap in A = cos(a,b) = a·b||a||||b||

Apply k-means clustering using:log(av. thread length)log(av. post length)asker/replier overlap

ExpertiseSharing


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Conclusion

k-means clustering of categories

We selected 3 categories which were stably placed inseparate clusters: programming, marriage, and wrestling

0.0 0.1 0.2 0.3 0.4 0.5 0.6

05

1015

2025

30

asker/replier overlap

aver

age

thre

ad le

ngth

Wrestling

Programming

Marriage

ExpertiseSharing


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Conclusion

differences in interaction: a matter of degree

Botany: red (posted mostly answers), blue (posted mostlyquestions), size (# of posts)

ExpertiseSharing


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Conclusion

differences in interaction: a matter of degree

In each category users differed according to how manypeople they replied to or received replies from

100 101 102 103

10−4

10−2

100

indegree

cum

ulat

ive

dist

ribut

ion programming

marriagewrestling

100 101 102 103

outdegree

programmingmarriagewrestling

ExpertiseSharing


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Conclusion

ego networks: programming

The answerers do not reply to one another.

ExpertiseSharing


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Conclusion

ego networks: marriage & divorce

A hint of generalized reciprocity: married or divorced userssometimes offer advice & support to one another.

ExpertiseSharing


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Conclusion

ego networks: wrestling

Discussion leads to mutual interaction:users engage through asking & replying

ExpertiseSharing


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Conclusion

how the categories relate to one another

25 Local Businesses24 Dining Out23 Travel22 Food & Drink21 Home & Garden20 Health19 Family & Relationships18 Beauty & Style17 Pregnancy & Parenting16 Entertainment & Music15 Society & Culture14 Arts & Humanities13 Education & Reference12 Science & Mathematics11 Social Science10 Politics & Government9 News & Events8 Games & Recreation7 Consumer & Electronics6 Computers & Internet5 Yahoo! Products4 Cars &Transportation3 Business & Finance2 Sports1 Pets

2524232221201918171615141312111097654321 25242322212019181716151413121110976543218 8

(a) (b)

(a) people who answer in i answer in j(b) people who answer in i ask in j

ExpertiseSharing


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Conclusion

a measure to reflect breadth: entropyHome Research Blank Blog Links

0.3 0.7

0.1 0.2 0.7

beauty & style

hair


maintenance & repairs car audio

L = 1

L = 2

}

}

0.3 0.7

0.1 0.2 0.7

beauty & style

hair


maintenance & repairs car audio

L = 1

L = 2

HL entropy for each level

HL = −∑

i pL,i log(pL,i)

And then sum over the levels.HT =

∑L HL

ExpertiseSharing


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Conclusion

Distribution of entropies for users with >= 40answers

entropy

number of users

0 1 2 3 4 501000

3000

5000

ExpertiseSharing


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Conclusion

Low entropy user: HT = 0

All answers are inthe Pets>Dogssubcategory

ExpertiseSharing


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Conclusion

Medium entropy user: HT = 2.08

TravelAsia Pacific 1

PetsPets General 1

SportsCricket 18

Society & CultureCultures & Groups 1Religion & Spirituality18

Arts & HumanitiesPhilosophy 1

L1 entropy: 0.99L2 entropy: 1.09

ExpertiseSharing


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Conclusion

Medium entropy user: HT = 2.33

Beauty & StyleMakeup 7General 3Fashion &Accessories 12Hair 7Skin & Body 3

Family & RelationshipsSingles & Dating 6Friends 1Family 1


ExpertiseSharing


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Conclusion

High entropy user: HT = 5.75


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Conclusion

Does focus matter?

Metric: percentage of best answersbest answer is selected by askerotherwise is put up to a vote by the community

Answer: No, combined entropy is uncorrelated withpercentage of best answers.

ExpertiseSharing


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Conclusion

Does focus matter for certain types ofcategories?

Metric: percentage of best answers in categorycompute H2 limiting to a single L2 category

level 1 category(ies) Pearson ρ p-value(entropy, % best)

computers & internetscience & math −0.22 10−7

family & relationships −0.13 10−13

sports −0.01 0.65

Answer: Yes, focus does matter when answers sought arefactual.

ExpertiseSharing


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Conclusion

What else matters in predicting best answers?

No matter whether what one seeks is facts, advice, support,or conversation, lengthier replies are appreciated.

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best not best

5020

010

0050

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char

acte

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ngth

of a

nsw

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Conclusion

What else matters in predicting best answers?

Programming Marriage Wrestlinganswer length + + +thread length − − −# previous best answers + + +# previous answers − − −R2 0.729 0.693 0.692

The difference: for programming, the users’ past trackrecord is more predictive.

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Conclusion

summary of Yahoo! Answers findings

everyone knows somethinguser interactions differ by categorysome users are more focused than others (and itmatters in technical matters)simple metrics hold predictive power for best answers

ExpertiseSharing


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Conclusion

Outline

1 motivation

2 related work




6 Conclusion

ExpertiseSharing


motivation

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Conclusion

Competing to share expertise on Witkey sites

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Conclusion

User prestige networks

previously (Java Forum): asker − > replierin Task CN: submitter − > winner

If one of 2 people wins in two tasks, same person wins 77%of the time.

ExpertiseSharing


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Conclusion

Task prestige networks

If winners of other tasks lose in this task, this task is moreprestigious...

ExpertiseSharing


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Conclusion

Participation matters

3,100 tasks in total (design, business strategy,programming...)

more participants, lower average “ExpertiseRank"more participants, higher winner’s “ExpertiseRank"

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Conclusion

Money matters

log(money)

6 7 8 9 10 11

0.64***

02

46

810

0.43***6

78

910

11

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log(views) 0.77***

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0 2 4 6 8

02

46

8

log(submissions)

ExpertiseSharing


motivation

related work






Conclusion

Who will win?

variable β

log(# submissions) −0.151log(# other attempts by user) −0.135# other wins by user 0.029

R2 = 0.708

ExpertiseSharing


motivation

related work






Conclusion

Strategy matters

Winners tend to choose less popular tasks, and bothwinners and losers adjust over time.

ExpertiseSharing


motivation

related work






Conclusion

Strategy pays

People who win 5 or more times narrow the intervalsbetween wins.

ExpertiseSharing


motivation

related work






Conclusion

Outline

1 motivation

2 related work




6 Conclusion

ExpertiseSharing


motivation

related work






Conclusion

Network analysis can be used to understand expertisesharingIn forums where factual expertise is shared, expertisecan be identifiedIn Y! Answers more than factual expertise in beingsharedWhen money is at stake, users behave strategically

ExpertiseSharing


motivation

related work






Conclusion

future work

augmenting interfaces to match expertisetracing replier strategies over time in question selectionanalyzing the role of community in QA forums

ExpertiseSharing


motivation

related work






Conclusion

thanks!

Jun Zhang http://www-personal.umich.edu/~junzh

Mark Ackerman http://www.eecs.umich.edu/~ackerm

Eytan Bakshy http://www-personal.umich.edu/~ebakshy

Jiang Yang http://www.jiangyang.us

Supported by: ARI, NSF 0325347

http://www-personal.umich.edu/~junzh

http://www.eecs.umich.edu/~ackerm

http://www-personal.umich.edu/~ebakshy

http://www.jiangyang.us

Documents

Expertise Sharing Dynamics in Online Forums - Lada Adamic · Sharing Dynamics in Online Forums motivation related work Java Forum: inferring expertise Yahoo Answers Clustering categories