1. Social networks and information sharing Lada Adamic

2. Outline Individual contributors: Is narrow focus of benefit? Should they draw on knowledge in other disciplines? What motivates individuals to volunteer information? How does money and competition affect that motivation? The network: What role do social networks play in information diffusion? Are online networks truthful and representative of trust? The information: How does information change as it diffuses?

3. Focus and quality in knowledge contribution study 4 knowledge contribution contexts scholarly publications (1900-2008) patents (1976-2006) Q&A forums (Yahoo! Answers, Baidu, Naver) Wikipedia main finding: focus trumps quantity in explaining quality of contribution Knowledge iN Adamic, Wei, Yang, Nam, Clarkson, First Monday, 2010 Knows

4. Focus and knowledge contribution PATENTSPAPERS WIKIPEDIACQA

5. Impact of information diffusion across communities study scholarly citation networks (JSTOR, patents) does drawing from other areas (i.e. citing outside one’s field) translate to having higher impact? social sciences and humanities: no natural sciences and patents: yes Shi, Adamic, Tseng, PLoS One, 2009

6. Motivation and quality in information sharing Analyze 2.6 million questions, 4.6 million answers + 26 interviews of top answerers Users who contribute more often and less intermittently contribute higher quality information Users prefer to answer unanswered questions and to correct incorrect answers What motivates individuals to contribute in online Q&A forums? altruism learning competition (point system) Knowledge iN Nam, Ackerman, & Adamic, CHI’2009

7. Monetary incentives and contribution Crowdsourcing: 120 translation tasks all pay auction mechanism: participants contribute solutions, only 1 is selected to receive payment price treatment: high, low shill treatment: enter in our own solution as a user with or without prior success results monetary incentives incentivize spam (85% are machine translations) higher pay yields better contributions shills discourage other quality contributions Liu, Yang, Adamic, Chen

8. Social dynamics of information in virtual spaces (e.g. Second Life) Items diffusing through social network spread more rapidly but have limited range Early adopters are distinct from connectors Sellers who chat with customers enjoy more repeat business, but social interaction doesn’t scale  Bakshy, Karrer Adamic EC’09,  Bakshy,Simmons,Huffaker,Teng, Adamic, Best Paper @ ICWSM’10  Bakshy, Karrer Adamic EC’09,  Bakshy,Simmons,Huffaker,Teng, Adamic, Best Paper @ ICWSM’10

9.  Goal: Understand basis of trust and friendship Understand causes of bias in online trust ratings  Data: 600K CouchSurfing users, 3 million ratings Amazon & Epinions ratings  Findings: When ratings are public, and when there is potential for reciprocity, ratings are overly positive Can online social networks be used as a proxy of trust and reputation? I rate you. You rate me. Should we do so publicly? WOSN 2010

10. Design choices ?? ??

11. Reciprocity in CouchSurfing Public friendship ratings are more highly correlated (rho = 0.73) than private trust ratings (rho = 0.39)

12. Can trust be equated with friendship? Close friendship -> high trust High trust -> variable friendship Trust and friendship strengthen over time, but rate varies by individual

13. Predicting trust (person to person “vouches”) Trust is localized/contextual Trust is not the same as friendship Global metrics do not perform as well as local ones VariablePredictive accuracy: Friendship degree67.7% Jaccard coefficient55.8% 2-step vouch propagation54.2% PageRank50.6%

14. Even “truthful” ratings may be biased

15. tracing information across the web duration in days length of phrase  How do memes change as they diffuse  length  sentiment  content  How does their diffusion/evolution depend on the underlying network structure?  Simmons, Adar, Adamic  Simple Polya’s urn model of copying ABCDEFGH CDEF DEFCDEF BCDE

16. For more information http://netsi.org Networks research group at the School of Information, University of Michigan http://netsi.org ladamic@umich.edu Students: Xiaolin Shi (PhD 2009, now postdoc at Stanford) Chun-Yuen Teng (current PhD student) Matthew Simmons (current PhD student) Xiao Wei (MSI 2010)