Download - FeedMe: Enhancing Directed Content Sharing on the Web

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Page 1: FeedMe: Enhancing Directed Content Sharing on the Web

Michael Bernstein, Adam Marcus, David Karger, Rob MillerMIT CSAIL

MIT HUMAN-COMPUTER INTERACTION

Enhancing Directed Content Sharing on the Web

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Information Overload

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You want more information.

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Aggregate

Filter

Facet

Recommend

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Friendsourced content sharing

Related to your research

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Related to your research

Friendsourced content sharingis inhibited.

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Our goal is to encourage friendsourced content sharingby making it easier and less inhibited.

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http://feedme.csail.mit.edu

1. Recommend recipients to reduce the time and effort for sharing

2. Surface activity via awareness indicators

3. Learn personalized models passively

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• Introduction• Related Work• Understanding Sharing• Supporting Sharing• Implementation• Evaluation• Discussion• Conclusion

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Related work

• Mediating our information access– Information mediators [Ehrlich and Cash 94]

– Contact brokers [Paepcke 96]

– Technological gatekeepers [Allen 77]

• Information is shared via e-mail [Erdelez and Rioux 00] to educate and form rapport [Marshall and Bly 04]

• Recommender systems focus on discovery [Resnick et al 94, Joachims et al 97]

• Expertise recommenders focus on information needs [McDonald 00]

• The FeedMe namesake [Burke 09, Sen 06]

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What drives social sharing?

Two surveys (N=40 / N=100) on Amazon Mechanical TurkVetted for cheatersPaid $0.20 / $0.05

IntroUnderstandingSupportingEvaluationDiscussionFe

edM

e

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E-mail is still dominant

E-m

ail

Talkin

g in

per

son

Social

net

wor

k site

s

Inst

ant M

essa

ge

Twitt

er

Blog

ging

pla

tform

s

News ag

greg

ator

s

Social

boo

kmar

king

Stum

bleU

pon

RSS/F

eed

Reade

r0

10

20

30

40

Which tools do you use regularly to share web content?

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Recipients want more

When asked to agree/disagree with:“I would be interested in receiving more relevant links.”

Median = 6

1 2 3 4 5 6 7

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1. Sharers are those who seek out large volumes of web content

2. Sharers are especially social individuals

Hypotheses

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What explains interest in sharing?

Sharing“I often tell people I know about my favorite web sites to

follow. “

Seeking“I often seek out entertaining posts, jokes, comics and videos using the Internet. “

Bridging social capital“I come in contact with new people all the time.”

Bonding social capital“There is someone I can turn to for advice about making very important decisions.”

[Ellison et al. 2007]

4 scales of 10 questions each

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β p-valuefactorSeeking .74 .001

Bridging Social Capital

.22 .05

Bonding Social Capital

.01 .33 Adj. R2 = 0.56

<

<

1. Sharers seek out large amounts of web content

2. Sharers are especially social individuals

Hypotheses

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IntroUnderstandingSupportingEvaluationDiscussionFe

edM

e

Can we give active content seekers the means to share more?

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RecommendationsAnnotate each post with friends who might be interested in the content

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Recommendations

[email protected] [email protected] FeedMe today 0 FeedMes today

[email protected] FeedMes today

Type a name…

Add an optional comment… Now

Later

Lifehacker: Share with friends using MIT’s FeedMe

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Awareness indicators

[email protected] FeedMes today

Address concerns about volume:“How much are we sending them?”

Give an indication of whether it’s old news“Oh, somebody already sent it to them?”

[email protected] FeedMes today

[email protected] it already

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Digests: managing volumeShare without overwhelming the inbox

Now Later

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One-click thanksLow-effort recipient feedback

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Implementation

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[email protected]

Building models without recipient involvement

MIT HCIResearch

Computer Science

Education

MIT HCIResearch

[email protected]

Computer Science

[email protected]

FeedMe Profile

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Recommendation details

design: 184tweet: 170web: 79

twitter: 48social: 43friendfeed:

32blog: 25

developer: 23

sports: 200baseball: 150

sox: 132lacrosse: 89workout: 41muscle: 30hiking: 23vitamin: 22

[email protected]:

[email protected]:twitter: 38tweet: 30social: 27post: 23

conversation: 19answers: 10blog: 3google: 1

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What impact does FeedMe haveon friendsourced sharing?

Two-week study for $3060 Google Reader users (46 male) recruited through blogsUsed Google Reader daily for two weeks with FeedMe installedViewed 84,667 posts; shared 713

IntroUnderstandingSupportingEvaluationDiscussionFe

edM

e

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2x2 Study design

• Recommendations (within-subjects)

• Awareness and feedback (between-subjects)

vs. vs. vs.

vs.

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Do shared posts benefit recipients? • Surveyed 64 recipients, who reported

on 160 shared posts• 80.4% of posts contained novel

content• Appreciative of having received the

post

1 2 3 4 5 6 70

1020304050

Post Ratings

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Are the recommendations worthwhile?

Speed, Keyboard-Free

Visual Clutter

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Do overload indicators [email protected]

5 FeedMes [email protected]

Saw it already

We asked: “What killer feature would get you to use FeedMe more?”

We measured: unprompted responses regarding social inhibition

14 of 28 without awareness+feedback features asked for them 3 of 30 with awareness+feedback features asked for them

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One-click thanks

30.9% of shares received a thanks

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Discussion

Mixed-initiative social recommender systemsE-mail as a delivery mechanism

IntroUnderstandingSupportingEvaluationDiscussionFe

edM

e

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Mixed-initiative social recommenders

• Humans filter recommendations for their friends

• Small marginal cost:sharers have already read the article

AI Friend Recipient

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Mixed-initiative social recommenders

• Sharers appreciate recommendations• High error tolerance

• Applications to other AI-hard problems

[Bernstein et al. UIST ‘09]

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Low-priority Queue

E-mail as a delivery mechanism“I'm pretty conservative about invading people's email space.”

“I feel that articles that I read are more like ambient information.”

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Summary of contributions

• Formative understanding of the process behind link sharing

• Leveraging social link sharing to power a content recommender

• Users as lightweight recommendation verification for others

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http://feedme.csail.mit.edu

http://bit.ly/CHIProgram2010

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Study designW

ithin

-subje

cts

Between-subjects

38

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39

FeedMe Not Installed: 93.8%

FeedMe Installed: 6.2%

Post Recipients

Bootstrapped Learning

30.9% One-click Thanks

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Topic relevance drives enjoyment

Questionable content quality

It's awkward

I sent too much already

Too much effort

Might have seen it already

Unsure of relevancy

0 2 4 6 8 10 12 14

What is the biggest concern you have when sharing?

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Topic relevance drives enjoyment“Those who know my politics usually send me very pointed articles – no junk.”

“I could care less about a cat boxing.”

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Seekingx 10

Sharing x 10

Bridgingx 10Bondingx 10

Verify scale agreementnormality assumptionshomoscedascicityfactor loading

Multiple regression on sharing index

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12

34

56

7Sharing

1 2 3 4 5 6 7Seeking

β p-valuefactor

Seeking .74 < .001

Bridging Social Capital

.22 < .05

Bonding Social Capital

.01 .33

Adj. R2 = 0.56

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Hypotheses

1. Sharers seek out large amounts of web content

2. Sharers are especially social individuals

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Hypotheses

1. Sharers seek out large amounts of web content

2. Sharers are especially social individuals

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FeedMe’s target usersSharers: firehose• Purposely consume volumes of

content• Use aggregators like Google Reader

Recipients: drip• Won’t use a new tool, but read e-mail

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Learn from intersection of recommendations

Privacy

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