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Information Sharing in Social Media

MURI 07. Information Sharing in Social Media. Xiao Wei in collaboration with Lada Adamic & NETSI Group School of information, University of Michigan. MURI 07. Ingredients in facilitating information sharing and trust building.

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Information Sharing in Social Media

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

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

  3. 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 MURI 07, University of Michigan

  4. 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. MURI 07, University of Michigan

  5. 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 MURI 07, University of Michigan

  6. 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: • Who is doing the vouching? • Who is being vouched for? • Can we predict which connections are vouched? • Dataset: • 600,000+ users, 1.5 million+ friendship connections MURI 07, University of Michigan

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

  8. MURI 07 – 2. Surfing a Web of Trust: Reputation and Reciprocity on CouchSurfing.com Major findings: • Results from logistic regression for each variable alone: • Global measures are poor predictors of whether an edge is vouched MURI 07, University of Michigan • 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

  9. 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. MURI 07, University of Michigan

  10. 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: • Patents and Research Articles: Normalized citation count • Wikipedia: New word contributed that survive revisions • 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 MURI 07, University of Michigan

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

  12. 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? MURI 07, University of Michigan 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 – 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 MURI 07, University of Michigan

  14. MURI 07 – 4. Information Diffusion in Citation Networks Major findings: 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. Correlations between impact and community proximity MURI 07, University of Michigan *** and * denote significance at < 0.001 and > 0.05 level respectively. • 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.

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

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