u niversity of m innesota altruism, selfishness, and destructiveness on the social web grouplens...

UNIVERSITY OF MINNESOTA

Altruism, Selfishness, and Destructiveness

on the Social Web

GroupLens ResearchUniversity of Minnesota

John Riedl


Bowling Alone (Amazon reviews)

Adaptive Hypermedia 20084

Tags scale:• Library of Congress: 20M books in 200

years.• www.librarything.com: 22M books in 3

years.Tag draw relevance from “the wisdom of crowds”


Messages

Community-maintained Artifacts of Lasting ValueoRequires User Modeling and Adaptive

Hypermedia

Key Research Challenges:oAttract contributionsoMaintain qualityoAchieve agreement


Alexa Germany


1. Google (German)3. Google (English)

Search


Google PageRank

Value of a page is the value of the pages that link to it

Recursive!

Algorithms and PsychologyThe Rich get Richer


Web Structure


(Web Search)shared

Maurice Coyle and Barry SmythAH’08


Research Questions

How can we mine free activity?What are the risks in these data?


2. YouTube

Video by Amateurs


Chocolate Rainby Tay Zonday

Adam Bahner, a Ph.D. student in American Studies at the University of MinnesotaNumber 2 hottest viral video in historyoHottest viral video of Summer 2007oOver 26 million views


Videos Life Fast, Die Young


Huberman Dynamics of Viral MarketingThe Dynamics of Viral Marketing,

ACM TWeb 2007, Leskovec et al., HP


Maximizing the Spread of Influence through a Social Network, David Kempe, Jon Kleinberg,

Éva Tardos, KDD’03

Independent Cascade Modelo Information diffuses over timeo Each neighbor who converts has a

one-time chance to convert others

Linear Threshold Modelo Each node considers the preferences

of all neighborso If total weight passes threshold, a

node converts


Video suggestion and discovery for YouTube: Taking random walks through the view graph

Shumeet Baluja, et al., Google, WWW 2008


Research Questions

How do preferences propagate naturally?What predicts fads?How do recommenders influence propagation?


4. Ebay

Online AuctionsCustomers Selling to Customers


Google Trends Front Page


4Chan vs. eBaumsWorld

4ChanoGoogle Trends HackoChocolate Rain

eBaumsWorldoMany other hackso “copyright” fight with 4chan


The Internet is Serious Business

“A phrase used to remind those who voluntarily leave the house that being mocked on the Internet is, in fact, the end of the world.”- Encyclopedia Dramatica


Amazon Robertson

shilled


The Information Cost of Manipulation-Resistance in Recommender Systems Resnick and Sami. ACM RecSys 08.

The Social Cost of Cheap PseudonymsFriedman and Resnick, Journal of Economics and Management Strategy, 2001


Increasing Contributions


What Theory Tells Us…Collective Effort Model People will contribute more if:

They believe their effort is important to the group

They like the groupSmaller is Better Slovic, Fischhoff, & Lichtenstein, 1980 People feel greater concern when the

reference group they’re part of grows smaller.

Specificity Matters Small & Loewenstein, 2003 Specific identity of those helped is

important in drawing people’s support.


CommunityLab Research

Social science to increase contributions Accessible to designers Algorithms, interfaces, toolkits

GroupLens @ Minnesota Recommender algorithms and

interfaces John Riedl, Joe Konstan, Loren Terveen

Bob Kraut and Sara Kiesler @ CMU Social psychology of computer use

Paul Resnick and Yan Chen @ Michigan


VOICE 2 Screen shotNumerical values are represented

by smilies

Who the contribution helps

Value of each contribution


Results

Want Smilies on the regular interface?

Self-report

1: Strongly Disagree2: Disagree3: Neutral4: Agree5: Strongly Agree

1 2 3 4 5

Self 3.87

All MovieLens 3.13

Similar Group 2.97

Dissimilar Group

2.94

Control 2.68

0% 5% 10% 15% 20%

Probability of rating a movie

Behavioral data

Self 7.2%

All MovieLens 10.2%

Similar Group 15.8%

Dissimilar

Group 5.9%

Control 7.4%


Research Questions

How can contributors be motivated?How can social attacks be mitigated?oMail list “unsubscribe”

How does social psychology interact with defense algorithms?oCan the griefers be encouraged to

give up?

Can freedoms be preserved?


5. Yahoo!

Everything


Flickr Popular Tags


Tag Selection Algorithms

“The Quest for Quality Tags”S. Sen, F. Harper, A. LaPitz, J. RiedlGROUP 2007


Catcher in the Rye

Huge number of tagsRQ: How can a tagging system show users tags

they want to see?


Users don’t agree

Most controversial tags (Bayesian expected entropy):tag entropy # #

comedy 0.987 28 30

classic 0.986 25 24

stylized 0.983 20 21

nudity (full frontal) 0.980 18 20

romance 0.980 18 17

quirky 0.977 25 20

magic 0.974 18 15

animation 0.974 26 20

Steven Spielberg 0.973 12 12

sci-fi 0.972 14 17


Tag PredictionRandom baseline: 21%

Implicit features:number of applications (39%)number of users (51%)number of searches for a tag (44%)number of users who searched for a tag (48%)length of tag (42%)

Moderation-based features:global average rating for a tag (59%)user-normalized global average rating for a tag (62%)tag reputation (57%)

Hybrid combinations: logistic regression, decision trees (67%)


Research Questions

How can a system distinguish between “good” tags and “bad” tags?How should quality control work?Can folksonomy be encouraged? o Showing users more tags leads to more

vocabulary reuse oHow much convergence is valuable?


6. Wikipedia

Next slide, please!


Wikipedia on Wikipedia


Wikiality on MySpace

1:20 – 2:15: edit wikipedia to make truth“What if the number of elephants in Africa were increasing?”

http://www.comedycentral.com/videos/index.jhtml?videoId=72347

http://www.comedycentral.com/videos/index.jhtml?videoId=72347


Creating, Destroying, and Restoring Value in

WikipediaGroup 2007

Reid PriedhorskyJilin ChenShyong (Tony) K. LamKatherine PancieraLoren TerveenJohn Riedl


Who contributes Wikipedia’s value?

User:Maveric149

3.8 million least frequent

editors0.5% of value 14% of valueWales

Swartz


PWV contributions of elite editors


Research Questions

How can vandalism be detected?How efficient is Wikipedia?How much conflict is valuable?


7. Studiverzeichnis

Social Network


The Predictive Power of Online Chatter

• Gruhl, Guha, Kumar, Novak, Tomkins

• Yahoo• ACM KDD 2005

• Volume of blog postings predict sales rank of books

• Queries can be automatically generated in many cases.

• Can sometimes predict spikes in sales rank.


Anti-aliasing on the Web

Jasmine Novak, Prabhakar Raghavan, Andrew Tomkins.

WWW 2004


ZipBirthdat

eSex

Story: Finding Medical Records (Sweeney 2002)

Medical Data EthnicityVisit DateDiagnosisProcedureMedicationTotal Charge

Voter List NameAddressDate

registeredParty

affiliationDate last

voted

ZipBirthdate

Sex

Former Governer of Massachussetts!


Risk of Information Exposure (Frankowski et al., SIGIR ‘06)

Sparse Dataset 1: private

YOU

Sparse Dataset 2: public

YOU

+ +

= Your private data revealed!

Combining algs

Keep private information within domain!


MovieLens Forums

- Started June 2005

- Users talk about movies

- Public: on the web, no login to read

- Can people identify these users in our anonymized dataset?


Research Questions

Can users be identified from the personal recommendation data? YESCan the datasets be redacted to protect the users? UNKNOWNCan the users be warned in time? OPEN QUESTION


Quantity

Quality

Tags

Social

Identity

Research Practice

Concept Understanding


Messages

Community-maintained Artifacts of Lasting ValueoRequires User Modeling and Adaptive

Hypermedia

Key Research Challenges:oAttract contributionsoMaintain qualityoAchieve agreement


Acknowledgements GroupLens

o John Riedl, Joe Konstan, Loren Terveen o Dan Cosley, Shilad Sen, Tony Lam, Rich Davies, Dan Frankowski,

Max Harper, Sara Drenner, Al Mamunur Rashid, Sean McNee, Reid Priedhorsky, Aaron Halfaker

CommunityLabo Sara Kiesler, Bob Kraut, Paul Resnick, Yan Chen

NSFo DGE 95-54517, IIS 96-13960, IIS 97-34442, IIS 99-78717, IIS 01-

02229, IIS 03-24851, IIS 05-34420, IIS 03-25837

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