Professor Hendrik Speck - Social and Virtual. - An Analysis Framework for Large Scale Communities. Commercial Communities Conference. Technical University of Berlin. Institute of Sociology, October 30 - 31 2008, Berlin, Germany, User Generated Content, Interaction, Third Party Associations and Content, Access and Connectivity, API's, Beacons, and Data Feeds, Merger of Social, Mobile and Local, social network analysis, social network visualization, Audience and Participants, Relational Data, Mathematical Models, Analytical Framework, Processing, Computing Power, Computer Mediated Communication, Visualization Algorithms, Interest, Use Cases, Marketing, Commerce, Web Services, Type of Data, Attribute, Ideational, Relational, Research Method, Survey Research, Surveys and Interviews, Ethographic Research, Observations, Field Studies, Documentary Research Logfiles, Texts and Archives, Type of Analysis, Variable, Typological, Network, User Profiles. Name, Age, Links, Interests, Hobbies, City, Country, Category, Videos Headline, Content, Descriptions, Tags, Playlists, Video Comments Author, Text, Tags, Themes, Ranking of Users and Channels Views or Subscriptions by Time and Category, Rankings of Videos Ratings or Views by Time and Category, Interaction Friends, Subscription, Comments, FollowUps Betweenness, Centrality Closeness, Centrality Degree, Flow Betweenness Centrality, Centrality Eigenvector, Centralization, Clustering Coefficient, Cohesion, Contagion, Density, Integration, Path Length, Radiality, Reach, Structural Equivalence, Structural Hole, Islands

1. Social and Virtual. - An Analysis Framework for Large Scale Communities Prof. Hendrik Speck University of Applied Sciences Kaiserslautern Commercial Communities Conference. Technical University of Berlin. Institute of Sociology. October 30 th - 31 st 2008 Berlin, Germany

2. Large Scale Communities

3. 1 Audience and Participants 2 Relational Data 3 Mathematical Models 4 Analytical Framework 5 Processing/Computing Power 6 Computer Mediated Communication 7 Visualization Algorithms 8 Interest/Use Cases 9 Marketing/ Commerce/ Web Services Requirements. Social Network Analysis Social Networks

4. 0 % Critics 50 % 52 % 19% Collectors Joiners Spectators Inactives 33 % 19 % 15 % Web 2.0: Consumer Groups by Activity. United States. 2006. Creators 13% Publish Web page or blog / Upload video to videoportals Comment on blogs / Post ratings and reviews Use RSS / Tag Web pages Use social networking sites Read blogs / Watch videos / Listen to podcasts None of these activities Source: Li, Charlene. Social Technographics. Forrester Research. 2007. Available: http://www.forrester.com/Research/Document/Excerpt/0,7211,42057,00.html Social Networks

5. 1 Individual/Collective/Network 2 Technology and Algorithms 3 Retrieval of All Relevant Attributes 4 Maintenance of Relationships 5 Merger of Macro and Micro 6 Generic Clustering and Filtering 7 Real Time/ No Direct Feedback 8 Action/Communication vs. Intention 9 Assumed Identity/ Islands/Holes Large Scale Analysis Attributes Social Networks

8. Commercial Channels Traffic and Lead Acquisition. Source: Smirnoff Experience. YouTube Channel and Website. 2008. Transition of Attention, Recall, Experience, Brand, Membership, and Buying Impulse.

9. Brand and Commercial Channels

11. Brand and Commercial Channels

12. Commercial Channel Network Concentric Marketing. iTunes/Youtube/Facebook. Stanford University. Source: Stanford University at iTunes, YouTube and Facebook. 2008. Attention * Recall * Popularity * Brand * Participation * Insight

13. Analysis

14. 1. User Information and Interests Social Networks Data Mining. Data Layers. 2. User Generated Content, Interaction 3. Third Party Associations and Content 4. Access and Connectivity 5. API's, Beacons, and Data Feeds 6. Merger of Social, Mobile and Local

15. 1. Account ID 2. User Name 3. First Name 4. Last Name 5. Academic Title 6. Academic Degree 7. Sex/Gender 8. Birth/Maiden Name 9. Relationship Status User Identifiers and Attributes. Social Network Analysis. 10. Sexual Preferences 11. Birthday 12. Sign of the Zodiac 13. Hometown 14. Country 15. Time Zone 16. Political Views 17. Religious Views Social Networks

16. 18. Address 19. City 20. Zip 21. Country 22. Website 23. Email 24. Mobile Phone 25. Land Phone 26. Fax Contact Information. Social Network Analysis. 27. Skype ID 28. ICQ ID 29. AIM ID 30. Yahoo ID 31. WindowsLive ID 32. GoogleTalk ID 33. Gadu-Gadu ID Social Networks

17. 34. Status 35. Employer 36. Position/Title 37. Company Website 38. Address 39. City 40. Zip Code 41. State 42. Country Work. Social Network Analysis. 43. Industry 44. Description 45. Wants 46. Haves 47. Time Period From 48. Time Period To 49. Business Organization Social Networks

18. 50. College/University 51. Class Year 52. Attended for 53. Degree 54. College/Graduate School 55. Concentration 56. Second Concentration 57. Third Concentration 58. Degree Education. Social Network Analysis. 59. High School 60. Class Year Social Networks

19. 61. Activities 62. Interests 63. Hobbies 64. Favorite Music 65. Favorite TV Shows 66. Favorite Movies 67. Favorite Books 68. Favorite Quotes 69. About Me Personal Information and Interests. Social Network Analysis. 70. Pictures 71. Uploaded Picture(s) 72. Picture Tags 73. Audio 74. Uploaded Audio 75. Audio Tags 76. Video 77. Uploaded Video(s) 78. Video Tags Social Networks

20. 79. Location 80. Contacts 81. # of Contacts 82. Messages 83. # of Messages 84. Events 85. # of Events 86. Guestbook Entries 87. # of Guestbook Entries Connection and Usage Information. Social Network Analysis. 88. Online Status 89. Login Time 90. Usage 91. IP Address 92. Network 93. Operating System 94. Browser 95. Screen Size 96. Language Social Networks

21. Types of Data and Analysis Research Method Survey Research Surveys and Interviews Ethographic Research Observations, Field Studies Documentary Research Logfiles, Texts and Archives Social Networks Type of Data Attribute Ideational Relational Type of Analysis Variable Typological Network } Source: Scott, John. Social Network Analysis. Sage Publications. 2000. Paperback, ISBN: 0761963391 }

22. 1 User Profiles. (Name, Age, Links, Interests, Hobbies, City, Country, Category) 2 Videos (Headline, Content, Descriptions, Tags, Playlists) 3 Video Comments (Author, Text, Tags, Themes) 4 Ranking of Users and Channels (Views or Subscriptions by Time and Category) 5 Rankings of Videos (Ratings or Views by Time and Category) 6 Interaction (Friends, Subscription, Comments, FollowUps) Youtube. Relational Data. Social Network Analysis Social Networks

23. Visualization

24. Betweenness Centrality Closeness Centrality Degree Flow Betweenness Centrality Centrality Eigenvector Centralization Clustering Coefficient Cohesion Contagion Attributes and Quantities. Social Network Analysis. Density Integration Path Length Radiality Reach Structural Equivalence Structural Hole Islands Social Networks

25. Inflow. Social Network Analysis. 2003. Social Networks Source: Valdis Krebs and David Krackhardt. Inflow/ Kite Network. 2003, Available: http://www.orgnet.com/sna.html

26. Degrees/Centrality, Closeness, and Betweeness. Social Network Analysis. Social Networks Closeness Betweeness Degrees/Centrality

27. Visualisation of Gender, Relationships, Status, and Age Social Networks

28. Weight/ Direction/ Intensity of Relationships Social Networks

31. Gender Distribution in Percent and Age. StudiVZ. 2006/2008. Analysis Source: Hagen Fritsch. StudiVZ. Inoffizielle Statistikpräsentation. December 2006, Available: http://studivz.irgendwo.org/ and University of Applied Sciences Kaiserslautern. Analysis of Large Scale Communities. January 2008 5 2.5 7.5 10 15 2006 0 2006 Total 2006 12.5 2008 2008 Total 2008

32. 5 Male. Relationship Status in Percent and Age. StudiVz. 2008. Analysis Source: University of Applied Sciences Kaiserslautern. Analysis of Large Scale Communities. January 2008 2.5 0 Romance Open Relationship No Data In Relationship Married Single

33. 5 Female. Relationship Status in Percent and Age. StudiVz. 2008. Analysis Source: University of Applied Sciences Kaiserslautern. Analysis of Large Scale Communities. January 2008 2.5 0 Romance Open Relationship No Data In Relationship Married Single

37. Die Lokalisten. Social Network. 2007. Social Network Analysis Source: Heinen, Felix. Datenvisualisierung eines sozialen Netzwerks. Die Lokalisten. University of Applied Sciences Nürnberg. Diploma Thesis. 2007, Available: http://www.felixheinen.de/020.html

38. Die Lokalisten. Social Network. 2007. Social Network Analysis Source: Heinen, Felix. Datenvisualisierung eines sozialen Netzwerks. Die Lokalisten. University of Applied Sciences Nürnberg. Diploma Thesis. 2007, Available: http://www.felixheinen.de/020.html

39. End

40. Thank you for your attention. I will gladly answer your questions. Prof. Hendrik Speck contact (at) hendrikspeck [dot] com University of Applied Sciences Kaiserslautern Information Architecture Lab Amerikastrasse 1 66482 Zweibrücken Tel: +49 6332 914 360 Skype: hendrikspeck Conclusion Contact Information

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