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The ties that bind: Social network principles in online communities.

The ties that bind: Social network principles in online communities. Alan Fco . Diaz Hernandez Prof. Dr. Eduard Heindl. Content. First part -Keywords -Introduction -Background -Network theory and social capital. Second part -Slashdot -Model and hypothesis -User conduct

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The ties that bind: Social network principles in online communities.

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  1. The ties that bind: Social network principles in online communities. Alan Fco. Diaz Hernandez Prof. Dr. Eduard Heindl

  2. Content First part -Keywords -Introduction -Background -Network theory and social capital Second part -Slashdot -Model and hypothesis -User conduct -Hypothesis 1,2,3,4 -Research design and data -Results -Conclusions

  3. Whatis a SOCIAL NETWORK?

  4. Keywords. • Online communities. • Social Capital. • Structural Holes. • Reputation Systems . • Web 2.0 • Ronald Stuart Burt

  5. Introduction. • Web 2.0 (Wikipedia, Facebook, Slashdot). • The clientis faceless. • Online socialnetworkshadbecome a parallel worldtomanypeople.

  6. Social network theory's. Online social networks. Brokerage Closure.

  7. Background. Can a online social networkwhich is not much more than a network be considered an organization? Aristoteles. Granovetter. Ouchi.

  8. Network theory and social capital. • Social Network  Social capital • Online Social networks. ex. TWITTER

  9. Network Theory. • Burt  Theory of social capital in network by focusing on the presence or absence of structural holes. • BROKERAGE vs. CLOSURE

  10. CLOSURE vs. BROKERAGE.

  11. Studies (Brokerage). • Burt The social capital of French and American managers. • Zaheery bell  Benefiting from network position: firm capabilities, structural holes, and performance.

  12. Studies (Closure). • Ashleight y Nandhakumar  Trust and technologies.

  13. So which one it´s better????

  14. Closure • Brokerage

  15. Second part Study for the site Slashdot.

  16. Site which provides news of technology founded in 1997. • How it works? • What´s “KARMA”. • 2002Online social network.

  17. Model and hypotheses • The relationship between network structure and social capital. Social capital  KARMA • Brokerage  High between ness/low constraint. • Closure  Low between ness/High constraint.

  18. Users conduct • Constraint • Between-ness

  19. Research design and data. • 6000 users with over 200,000 relationships. • Standard regression of several variables like: comments, friend ratio, foe ratio and karma. • Using UCINET.

  20. Results.

  21. Results. • Respond Hypothesis 1.

  22. Results. • Respond Hypothesis 2.

  23. Results. • Respond Hypothesis 3.

  24. Results. • Respond Hypothesis 4.

  25. Conclusion • Structural Holes have an important role in a social network. • Brokerage  lower levels of karma. • Closure  higher levels of karma. • Based on advertising.

  26. Conclusion High Karma Lower Karma

  27. Questions?

  28. Results

  29. How is karma generated?.

  30. Hypothesis 1. A.-Most participants of the site will exhibit both low between-ness and low constraint. B.-There will be more participants with high constraint measures than with high between-ness measures. C.-There will be few individuals who score highly in both constraint and between-ness.

  31. Hypothesis 2. A.-High between-ness and high constraint are individually associated with high social capital. B.-High between-ness and high constraint are jointly associated with high social capital. C.-High constraint is more associated with high social capital than is high between-ness.

  32. Hypothesis 3. A.-Between-ness is inversely related to participation intensity. B.-Constraint is directly related to participation intensity. C.-Network investment moderates the relationship between both between-ness and constraint and social capital.

  33. Hypothesis 4. A.-Positive outcomes from between-ness are more significant to those with high social capital. B.-Positive outcomes from constraint are more significant to those with low social capital.

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