1. Information Sharing in Social Media Xiao Wei in collaboration with Lada Adamic & NETSI Group School of information, University of Michigan MURI 07

2. Research Projects MURI 07 1.Incentivizing individuals to meet each other’s information needs: User Behavior Dynamics: A Sustainable Mechanism Works for Baidu Knows 2.Building reputation and trust through online and offline interactions: Reputation and Reciprocity on CouchSurfing.com 3.Who is likely to contribute more valuable information? Individual Focus and Knowledge Contribution 4.Should one build on “local” information & knowledge or draw on other communities? Information Diffusion in Citation Networks Ingredients in facilitating information sharing and trust building

3. MURI 07, University of Michigan MURI 07 – 1. User Behavior Dynamics: A Sustainable Mechanism Works for Baidu Knows Seeking and Offering Expertise across Categories: A Sustainable Mechanism Works for Baidu Knows, ICWSM 09, San Jose. Baidu Knows: Largest Chinese QA site Virtual-point knowledge market Built-in community tools Motivations & Research Questions: First study on this successful QA site How virtual points can help incentivize knowledge sharing Users’ adaptive behavior patterns Dataset: Full history of Q&A during Dec, 2007~May, 2008 9.3 million questions (5.2 million of them are solved) 2.7 million users with 17.2 million answers

4. MURI 07, University of Michigan MURI 07 – 1. User Behavior Dynamics: A Sustainable Mechanism Works for Baidu Knows Major Findings: Answerers are incentivized by points, thus expertise can be better allocated to more important questions Users allocate points differently among questions: e.g., different categories Askers adjust price from initial question, and they can slightly improve the ability of buying answers per point In order to ask, users are driven to answer. Users who both ask and answer contribute most.

5. MURI 07, University of Michigan MURI 07 – 1. User Behavior Dynamics: A Sustainable Mechanism Works for Baidu Knows Conclusion: A reinforcement cycle forms: people contribute more, are rewarded, gain more experience, improve their performance

6. MURI 07, University of Michigan MURI 07 – 2. Surfing a Web of Trust: Reputation and Reciprocity on CouchSurfing.com Surfing a Web of Trust: Reputation and Reciprocity on CouchSurfing.com, SIN 09, Vancouver Previous works on group level: Bialski & Batorski (2006) examined which factors contribute to higher trust between CouchSurfing friends. Molz (2007) examined the sociological meaning of reciprocity in the context of hospitality exchanges. Research Questions:  Trust: o Who is doing the vouching? o Who is being vouched for? o Can we predict which connections are vouched? Dataset: 600,000+ users, 1.5 million+ friendship connections

7. MURI 07, University of Michigan MURI 07 – 2. Surfing a Web of Trust: Reputation and Reciprocity on CouchSurfing.com Major findings: A high number of vouches are between “CouchSurfing friends”. Friendship degree: 1= Haven’t met yet 2= Acquaintance 3= CouchSurfing friend 4= Friend 5= Good friend 6= Close friend 7= Best friend

8. MURI 07, University of Michigan MURI 07 – 2. Surfing a Web of Trust: Reputation and Reciprocity on CouchSurfing.com Major findings: Conclusion:  Friendship degree information is beneficial  Global measures may be useful in assigning overall reputation scores, but not for predicting if a specific person will vouch for another or not  Further work is needed to determine if vouches are given too freely Results from logistic regression for each variable alone:  Global measures are poor predictors of whether an edge is vouched VariablePredictive accuracy: Friendship degree67.7% Shared friend55.8% 2-step vouch propagation54.2% PageRank50.6%

9. MURI 07, University of Michigan MURI 07 – 3. Individual Focus and Knowledge Contribution Individual focus and knowledge contribution, working paper Previous works on group level: J. Katz, D. Hicks, Scientometrics 40, 541 (1997) B. Jones, S. Wuchty, B. Uzzi, Science 322, 1259 (2008). S. Wuchty, B. Jones, B. Uzzi, Science 316, 1036 (2007). R. Guimera, B. Uzzi, J. Spiro, L. Amaral, Science 308, 697 (2005). I. Rafols, M. Meyer, Scientometrics (2008). S. Page, The difference: How the power of diversity creates better groups, firms, schools, and societies (Princeton University Press, 2007). Motivations & Research Questions: First study on individual level To study whether an individual’s diversity is beneficial.

10. MURI 07, University of Michigan MURI 07 – 3. Individual Focus and Knowledge Contribution Goal: To measure the relationship between the narrowness of focus and the quality of contribution of individuals across a range of knowledge sharing systems. Approach: Focus (Stirling measure): Quality: o Patents and Research Articles: Normalized citation count o Wikipedia: New word contributed that survive revisions o Q&A forum participant: Win rate Datasets: JSTOR: 2 million articles plus 6.6 million citations Patents: 5.5 million patents filed between 1976~2006 Q&A forums: Crawled data from Yahoo! Answers, Baidu Knows, Naver KnowledgeIN Wikipedia: Meta-history dump file of the English Wikipedia generated on Nov. 4th, 2006, parsed 7% pages

11. MURI 07, University of Michigan MURI 07 – 3. Individual Focus and Knowledge Contribution Major findings: Conclusion: Across all systems we observe a small but significant positive correlation between focus and quality.

12. MURI 07, University of Michigan MURI 07 – 4. Information Diffusion in Citation Networks Information Diffusion in Citation Networks. Previous works: Visualization and quantification of the amount of information flow between different areas in science [Boyack, 2005], [Bollen, 2009]. Features of information flows in citation networks [Borner, 2004], [Rosvall, 2007]. Effects of collaborations across different universities, and team collaborations [Katz, 1997], [Wuchty, 2007]. Research Questions: What happens once information has diffused across a community boundary? Shi X, Adamic LA, Tseng BL, Clarkson GS, 2009 The Impact of Boundary Spanning Scholarly Publications and Patents. PLoS ONE 4(8): e6547. doi:10.1371/journal.pone.0006547

13. MURI 07, University of Michigan MURI 07 – 4. Information Diffusion in Citation Networks Goal: To study information diffusion within vs. across communities and its subsequent impact. Approach: Studying citation networks: the social ecology of knowledge – where information is shared and flows along co-authorship and citation ties. Articles/patents -> nodes; citations -> directed edges, from cited to citing Communities: JSTOR -> Journal discipline; Patents -> Categories Community proximity: Datasets:  IBM patent citation network and JSTOR citation network

14. MURI 07, University of Michigan MURI 07 – 4. Information Diffusion in Citation Networks Major findings: PatentNatural science Social scienceArts & humanities Overall correlation 0.062***-0.027***0.033***0.044*** Correlation after removing 0 impact -0.047***-0.072***0.040***-0.011* *** and * denote significance at 0.05 level respectively. Correlations between impact and community proximity Conclusion: A publication’s citing across disciplines is tied to its subsequent impact.  While risking not being cited at all, patents and publications in the natural sciences are more likely be higher impact when they cite across community boundaries  There is no such effect in the social sciences and humanities. If we focus on patents and natural science publications that have had at least a given level of impact, we consistently observe that citing across community boundaries leads to slightly higher impact.

15. Thank you! further information: http://www-personal.umich.edu/~ladamic http://weixiao.us/projects.html