Professor Hendrik Speck - Information Mining in the Social Web. Empolis Executive Forum, June 8th 9th 2009 Berlin Germany. social networks, social media, web 2.0, social network analysis, usage, audience, user, markets, revenues, google, youtube, myspace, wikipedia, attributes, search engines, marketing, lobbying, information mining, information retrieval, risk, law, security, branding, marketing, privacy, private sphere, public sphere, anonymity, surveillance, panopticon, sousveillance, hype, history, features, examples, captcha, security, cracking, data portability, decentralization

1. Information Mining in the Social Web . Prof. Hendrik Speck University of Applied Sciences Kaiserslautern Empolis Executive Forum June 8 th -9 th , 2009 Berlin, Germany

2. Social Media

3. Profiles and Platforms. Social Bookmarking Social Video Sharing Social Photo Sharing Social Encyclopedia Social Community Social Music Community Social Networks

5. 1. Rating 2. Customer Reviews 3. Share Your Images 4. Also Bought 5. Sales Rank 6. Update Info 7. Sponsored Links Social Web 8. Sell Yours Here 9. Add to Lists 10. Tell a Friend 11. Write a Review 12. Give Feedback 13. Citations Social Features. Google, Amazon and Beyond. Amazon.com. 2007

6. 14. Click a Tag 15. Your Tags 16. Customer Reviews 17. Rate of Review 18. Comment Review 19. Discuss Product 20. Amapedia 21. Listmania 22. Help Others/ Tag Item 23. Rate Item 24. Video Review 25. Avg. Customer Review 26. Customer Review 27. Rate Review Social Features. Google, Amazon and Beyond. Amazon.com. 2007 Social Web

7. 28. Category Directory 29. Subject Directory 30. Update Info 31. Your Account 32. Your History 33. Your Searches 34. Sponsored Links 35. Careers 36. Contact Us 37. Feedback 38. Also Bought 39. Sell Item 40. Join 41. Advertise Social Features. Google, Amazon and Beyond. Amazon.com. 2007 Social Web

8. Social Data

9. 1. User Information and Interests Social Networks Data Mining. Data Layers. 2. ??? 3. ??? 4. ??? 5. ??? 6. ???

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. Google Inc. 1999. Social Web

18. Google Inc. 2009. Payment Browser, Applications, Phones, Interfaces, Standardisation Personalisation Search Advanced Search Recruitment and Standardisation Mobile and Localisation Content, Communication and Social Graph Mapping Social Web Checkout Chrome, Pack, Android IGoogle, Tool- bar, Note- book, Health, Finance, RSS, Docs, Cal Web Images, News, Products, Blogs, Movies Summer of Code Mobile, Local, SMS Blog, Talk, Groups, Orkut, Video, Picasa, Youtube, Book, Scholar Earth, Maps, Sketchup ( )

19. Vision of Search and Information Mining Source: Siegel, Randy. “Google 2084. What Google's homepage may look like in 2084.” New York Times. October 10, 2005. Available: http://www.nytimes.com/imagepages/2005/10/10/opinion/1010opart.html Social Web

20. Social Network Analysis

21. 1. Brand Monitoring and Protection 2. Market Research, Competitive Intelligence 3. Customer Experiences, Opinions, and Insights 4. Network Structure and Opinion Leaders 5. Themes, Issues, and Events 6. Brand Loyalties and Purchase Decisions 7. Customer Relations and Support 8. Product Development and Quality Control 9. Communications, Marketing, and Metrics Usage and Purpose of Social Network Analysis. Social Web

22. 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

23. Social Network Analysis. Social Networks

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

25. Degrees/Centrality. Social Network Analysis. Social Networks

26. Betweeness. Social Network Analysis. Social Networks

27. Closeness. Social Network Analysis. Social Networks

28. Male vs. Female. Social Networks

29. Young vs. Old/Slim vs. Overweight/ Weak vs. Strong Social Networks

30. Relationships. Social Networks

31. Weight/ Direction/ Intensity of Relationships Social Networks

32. Social Network Analysis Enron. 2004. Source: Heer, Jeffrey. Exploring Enron. Visualizing ANLP Results. University of California. 2004, Available: http://jheer.org/enron/v1/

33. Social Network Analysis Enron. 2004. Source: Heer, Jeffrey. Exploring Enron. Visualizing ANLP Results. University of California. 2004, Available: http://jheer.org/enron/v1/

34. Social Network Analysis Enron. 2004. Source: Heer, Jeffrey. Exploring Enron. Visualizing ANLP Results. University of California. 2004, Available: http://jheer.org/enron/v1/

35. Social Network Analysis Purchase Patterns of Political Books. Orgnet. 2004. Source: Valdis Krebs. Divided We Stand... Still. Available: http://www.orgnet.com/divided2.html

36. Social Network Analysis Jefferson High School. United States. 2005 Source: Goetz, Kristina. “New teen study could help stop spread of sexually transmitted disease.” Columbia News Service. March 15, 2005, Available: http://jscms.jrn.columbia.edu/cns/2005-03-15/goetz-teensex

42. StudiVz. Age Distribution in Percent. 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

43. 5 Male. Relationship Status in Percent and Age. 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

44. 5 Female. Relationship Status in Percent and Age. 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

45. 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

46. 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

48. Relationships Among Scientific Paradigms. 2007. Social Network Analysis Source: Boyack, Kevin , Dick Klavans, and W. Bradford Paley. "Relationships Among Scientific Paradigms." Seed. March 7, 2007, Available: http://seedmagazine.com/news/2007/03/scientific_method_relationship.php

49. Relationships Among Scientific Paradigms. 2007. Social Network Analysis Source: Boyack, Kevin , Dick Klavans, and W. Bradford Paley. "Relationships Among Scientific Paradigms." Seed. March 7, 2007, Available: http://seedmagazine.com/news/2007/03/scientific_method_relationship.php

50. Social Network Analysis Massachusetts Institute of Technology. Touchgraph. 2007. Source: Touchgraph. Google Browser. Available: http://www.touchgraph.com/TGGoogleBrowser.html

51. Social Network Analysis Playboy Magazine. Touchgraph. 2007. Source: Touchgraph. Google Browser. Available: http://www.touchgraph.com/TGGoogleBrowser.html

64. Social Network Analysis spamCAN. Spam Analyzer. 2006. Source: Ralf Wagner, Katharina Gerhardt, Yvonne Schmittler, and Prof. Hendrik Speck. SpamCan. Spam Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/spamcanproject/

65. Social Network Analysis spamCAN. Spam Analyzer. 2006. Source: Ralf Wagner, Katharina Gerhardt, Yvonne Schmittler, and Prof. Hendrik Speck. SpamCan. Spam Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/spamcanproject/

66. Social Network Analysis PeerLo. Bittorrent Network Analyzer. 2006. Source: Andreas Augustin, Christian Becker, and Prof. Hendrik Speck. PeerLo. Bittorrent Network Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/peerlo

67. Social Network Analysis PeerLo. Bittorrent Network Analyzer. 2006. Source: Andreas Augustin, Christian Becker, and Prof. Hendrik Speck. PeerLo. Bittorrent Network Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/peerlo

68. PeerLo. Bittorrent Network Analyzer. 2006. Source: Andreas Augustin, Christian Becker, and Prof. Hendrik Speck. PeerLo. Bittorrent Network Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/peerlo Social Network Analysis

69. Analyzing Reality – The Real Social Web

70. 1. Crawling and Indexing 2. Parsing and Pattern Matching 3. Attributes and Indicators 4. Categorization 5. Statistics and Analysis 6. Mapping and Visualization 7. Interaction Interface Analyzing Reality Structural Anatomy

71. Analyzing Reality Berlin Police Department. Press Release Overview. Source: Berlin Police Department. Press Releases. June 28, 2008, Available: http://www.berlin.de/polizei/presse-fahndung/presse.html

72. Press Release. Berlin Police Department. Source: Berlin Police Department. Press Release. No. 103842. XML Feed. June 28, 2008, Available: http://www.berlin.de/polizei/presse-fahndung/_rss_presse.xml Analyzing Reality

73. Natural Language Processing. Press Release. Date , Time Category Borough , ID Category Time , Borough Location Time , Category Category Pressemeldung. Eingabe: 15.10.2008 - 11:55 Uhr . Laute Musik führte zu Cannabisplantage . Treptow-Köpenick. # 3175 Eine Cannabisplantage entdeckten Polizeibeamte in der vergangenen Nacht in Oberschöneweide , nachdem sie wegen ruhestörenden Lärms gerufen worden waren. Mieter eines Hauses in der Parsevalstraße hatten gegen 21 Uhr 30 die Polizei alarmiert, da sie sich durch laute Musik gestört fühlten. … Bei der Überprüfung des 20-jährigen Wohnungsinhabers stellte sich zudem heraus, dass er per Haftbefehl gesucht wurde. ... Analyzing Reality

75. Visualization and Interface Evolution of Icons and Meanings. Data Layers/Clustering Standard Pin Arson Theft Drugs Violence Murder Arrest Vandalism Diverse Different Cluster Sizes Scale and Clustering / More than one Incident

86. Implications and Considerations

87. Social networks and other companies’ “aggressive” attempts to target advertising according to users’ search behaviour risk damaging the internet industry’s reputation . „ „ Source: Edgecliffe-Johnson, Andrew. Google founders in web privacy warning. Financial Times. May 19 2008, http://www.ft.com/cms/s/0/9a877256-25de-11dd-b510-000077b07658.html Sergei Brin and Larry Page, Founder of Google

88. Die Kehrseite der Medaille ist aber, dass Kriminelle solche Portale zum Datendiebstahl oder zur Verbreitung von Viren und Trojanern für sich entdeckt haben und Verbraucher allzu sorglos ihre Daten preisgeben und diesen im wahrsten Sinne des Wortes Tür und Tor Öffnen. „ „ Source: Verband der deutschen Internetwirtschaft eco. eco warnt vor steigendem IT-Sicherheitsrisiko in so genannten Social Networks. Pressemitteilung. February 15, 2008, http://www.eco.de/verband/202_4290.htm eco-Geschäftsführer Harald A. Summa

89. Twitter Maxim Microsoft Facebook MySpace StudiVz Bebo Web.de Google Yahoo Captcha Effectiveness. 2009. Privacy and Security Simple / Unreadable / Unsafe Complex / Illegible / Safer. >>

90. Breaking StudiVz Captcha. Letter Recognition under Adverse Conditions. 2008. Noise. Standard primitive pattern/ Repeating background/ No texture, Limited number of colors, No perturbation / separate color key Glyphs. Limited number of fonts, Normal fonts, No deformation, Same font size, No overlap, Limited rotation of glyphs, Limited colors, Limited color variation, Same number of glyphs (Positive: Glyphs not aligned, Glyph position not constant, No words/ dictionary attacks) Social Network Analysis

91. Democratization of Personality Management. Mobbing in StudiVZ . 2008. Social Networks

93. (2) Der Nutzer räumt zoomer.de ein räumlich und zeitlich uneingeschränktes, kostenloses Nutzungsrecht an den von ihm auf dem Internetportal zoomer.de veröffentlichten Inhalten, insbesondere an den Diskussionsbeiträgen in Wort und Bild, den Bewertungen und Kommentaren ein. Das Nutzungsrecht erfasst insbesondere • das Recht, die Inhalte zu speichern und zu vervielfältigen und online im Internet auf zoomer.de sowie auf weiteren Internetportalen öffentlich zugänglich zu machen, soweit diese Internetportale auch von Unternehmen der Holtzbrinck-Gruppe (verbundene Unternehmen im Sinne von §§ 15 ff. Aktiengesetz) betrieben werden; Social Networks Rights Reserved by Platform. Zoomer.de Source: Holtzbrinck. Nutzungsbedingungen. Zoomer.de. Available: http://www.zoomer.de/news/nutzungbedingungen

94. § 8 Rechte an den von dem Nutzer veröffentlichten Inhalten, Nutzungsrechtseinräumung, Haftung des Nutzers für die Inhalte (1) Der Nutzer hat sicherzustellen, dass er über die Nutzunsgrechte an den von ihm auf dem Portal zoomer.de veröffentlichten Inhalten, insbesondere den Materialien, Texten, Bildern, Audio-Files oder Videos usw. verfügt. Social Networks Duties of User. Zoomer.de Source: Holtzbrinck. Nutzungsbedingungen. Zoomer.de. Available: http://www.zoomer.de/news/nutzungbedingungen

95. Selling 13000 Oxford Students by hanno 200,000 British users, 10k daily active, $121.31 made yesterday by sourcecode 175000 user app for sale - Dating and Sex Test by goldfinger App for sale: 2M+ users by appdev2008 Over than 6000$ December Revenue - App For Sale by Tiger 105,308 adds yesterday with over $400 revenue - in one day! by Temporary 250k users / 2000$ by tolga Facebook App + Website 90,000 users $25,000 income since August by darbsllim apps making 1000.00 a day! by webguy2008 My Clothing Label - 200k+ Users - $0 starting bid by trianaglobal Social Networks Application/Data Market Place. Facebook. Source: Facebook Platform Developer Forum . Application Marketplace. Available: http://forum.developers.facebook.com/

96. Social Networks Source: Mack, Daniel. StasiVZ: StudiVZ startet Spionage Werbung. Bündnis 90/Die Grünen. December 15, 2007, Available: http://danielmack.de/2007/12/15/stasivz-studivz-startet-spionage-werbung/ Brand Damage. StudiVz vs. StasiVz. 2007.

98. 1. Data must be Fairly and lawfully processed. 2. Processed for limited purposes. 3. Adequate, relevant and not excessive. 4. Accurate. 5. Not kept longer than necessary. 6. Processed in accordance with the data subject's rights. 7. Secure. 8. Not transferred to countries without adequate protection. Directive 95/46/EC of the European Parliament. Source: Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data. Official Journal. L 281, November 23, 1995, P. 0031 – 0050, Available: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:31995L0046:EN:HTML The Social Web

99. 1 Data Ownership. MySpace or Yourspace? 2 Data Visibility. Privacy / Youth Protection 3 Data Aggregation. Who can link? 4 Data Access. Who can change and delete? 5 Data Expiration. Forgetting and Forgiving. 6 Data Protection. Who guards and mediates? 7 Who controls and monitors? 8 Who rules and governs? Unanswered Questions: Data, Social, Politics, and Law. The Social Web

100. 1 Data Accuracy 2 Data Integrity, Reliability, and Compliance 3 Data Source, Transparency, and Trust 4 Data Ownership and Sharing 5 APIs, Coopetition and Standardization 6 Data Politics 7 Governance and Regulation 8 Social Norms and Values Business Considerations. The Social Web

101. End

102. Professor 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

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