“…challenge accepted paradigms to propose new ways of fighting from air, space, and cyberspace.”<br />- General Stephen Lo...
General Stanley McChrystal<br />“It’s not the number of people you kill, <br />it’s the number of people you convince.”<br />
Enhancing Soft Power (ESP)<br />Analysis and Situational Awareness over network of <br />Computers, Sensors, People, and C...
Network Visualization<br />Consider all challenges that can be addressed by visualization in the cyberspace<br />Cyberspac...
Institutions Involved<br />Ohio Center of Excellence on Knowledge-enabled Human Centered Computing (Kno.e.sis), UCI (Rames...
Networks in Cyberspace<br />Networks in Cyber Space<br />Sensor Networks<br />Computer Networks<br />Social Networks<br />
Networks and Cyber Security<br />Networks in Cyber Space<br />Cyber Security<br />Sensor Networks<br />Computer Networks<b...
Networks and Soft Power<br />Networks in Cyber Space<br />Computer Networks<br />Sensor Networks<br />Soft Power<br />Citi...
Hard Power – “Carrots and Sticks”<br />
Soft Power: “The Second Face of Power”<br />“Affect behavior through attraction”<br />“Soft power is attractive power”<br ...
How important is soft power?<br />Henry Crumpton– CIA covert-op leader<br />Hard power – 25%<br />Soft power – 75%<br />Wi...
Using soft power and monitoring its effects<br />One approach – Analyze  online social networks<br />Ohio State<br />Knuco...
Twitris: Architecture<br />
Twitris: Features and Research Issues<br />Data intensive<br />Processing and storage of data Vs meta data<br />Timeliness...
Twitris: Ongoing investigations<br />For a given viral “tweet” study<br />network of people who reply or re-tweet<br />do ...
Sparse and Dense ReTweet Graphs - Call for action vs. Information Sharing tweets<br />Showing retweet (forward chains) of ...
Sparse and Dense ReTweet Graphs - Call for action vs. Information Sharing tweets<br />Showing retweet (forward chains) of ...
Analysis of Conversation and Chatter <br />Use language constructs to analyze sentiments  and intentions<br />
Visual Analytics for Dynamic Interaction Networks<br />Model social networks as graphs<br /><ul><li>People are nodes
Their interactions are edges
Groups of connected people are communities (clusters)
Edge weights capture relative importance
Addition and deletion of edges and nodes represent changes in the network</li></ul>Challenges<br /><ul><li>Identify and lo...
Characterize the types of changes and potential causes
Facilitate interactive interrogation</li></li></ul><li>Events and Behaviors in Interaction Networks<br />Critical Events f...
Paradigm: Overview, Zoom, Filter, and DoD<br />Event detection proceeds iteratively:<br />Events are computed from time Ti...
Network Visualizer Architecture<br />
Coarse View of a Wikipedia Subgraph<br />
Zoom and Filter Snapshot<br />
Event View: Importance of Ranking<br />
Semantic Annotations for any resource in cyberspace (message, text, images, and AV)<br /><ul><li> Annotate resources with ...
 Use the metadata to process and elicit information from a given set of resources</li></li></ul><li>Automatic Semantic Ann...
 Type of company
 Industry affiliation
 Sector
 Exchange
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Enhancing Soft Power: using cyberspace to enhance Soft Power

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New Tools for a War for Peace - using cyberspace, social media and social science to enhance Soft Power

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Enhancing Soft Power: using cyberspace to enhance Soft Power

  1. 1. “…challenge accepted paradigms to propose new ways of fighting from air, space, and cyberspace.”<br />- General Stephen Lorenz<br />
  2. 2. General Stanley McChrystal<br />“It’s not the number of people you kill, <br />it’s the number of people you convince.”<br />
  3. 3. Enhancing Soft Power (ESP)<br />Analysis and Situational Awareness over network of <br />Computers, Sensors, People, and Content<br />for enhancing Soft Power<br />AmitSheth, LexisNexis Eminent Scholar <br />Director, Ohio Center of Excellence on Knowledge-enabled Computing (Kno.e.sis)<br />Wright State University<br />http://knoesis.org<br />
  4. 4. Network Visualization<br />Consider all challenges that can be addressed by visualization in the cyberspace<br />Cyberspace = {computers, sensors, people, conversations}<br />For issues effecting security in cyberspace and to enhancing military’s capacity to project soft power<br />So broadly, People-Content-Network Visualization<br />
  5. 5. Institutions Involved<br />Ohio Center of Excellence on Knowledge-enabled Human Centered Computing (Kno.e.sis), UCI (Ramesh Jain), OSU Faculty in Visual Analytics [Srini Parthasarthy] <br />WSU Faculty in Internet/WWW, Semantic Web, Sensor Web, Social Computing, Network Security [Bin Wang] , Visualization [Tom Wischgoll], and Cognitive Science [John Flach,…]<br />Optionally, faculty at Indiana University Center for Complex Networks & Systems, and Purdue University<br />Preliminary thoughts on COE on Enhancing Soft Power (ESP)<br />
  6. 6. Networks in Cyberspace<br />Networks in Cyber Space<br />Sensor Networks<br />Computer Networks<br />Social Networks<br />
  7. 7. Networks and Cyber Security<br />Networks in Cyber Space<br />Cyber Security<br />Sensor Networks<br />Computer Networks<br />Social Networks<br />
  8. 8. Networks and Soft Power<br />Networks in Cyber Space<br />Computer Networks<br />Sensor Networks<br />Soft Power<br />Citizen Sensing<br />Social Networks<br />
  9. 9. Hard Power – “Carrots and Sticks”<br />
  10. 10. Soft Power: “The Second Face of Power”<br />“Affect behavior through attraction”<br />“Soft power is attractive power”<br />Also called cooptive power: <br />the ability to shape the preferences of others<br />getting others to want the outcomes that we want<br />
  11. 11. How important is soft power?<br />Henry Crumpton– CIA covert-op leader<br />Hard power – 25%<br />Soft power – 75%<br />Wield soft power (to counter terror groups in Iraq and Afghanistan) through<br />Empowerment of native people<br />Developmental and educational projects<br />Measure impact of<br />policy alternatives<br />development initiatives<br />
  12. 12. Using soft power and monitoring its effects<br />One approach – Analyze online social networks<br />Ohio State<br />Knucomp<br />Visualize the interaction graphs formed in social networks <br />(more later)<br />Aggregate social perceptions<br />Analyze with a focus theme <br />Use spatio-temporal aspects<br />Twitris<br />How hard is this?<br />
  13. 13. Twitris: Architecture<br />
  14. 14. Twitris: Features and Research Issues<br />Data intensive<br />Processing and storage of data Vs meta data<br />Timeliness<br />Live resource aggregation<br />Near real time <br />Tunable spatio-temporal scope/weights<br />Global Iran election Vs. Local health care debate<br />Parallel programming, <br />cloud computing (map-reduce)<br />Information retrieval,<br />natural language processing (harder with casual text), machine learning<br />Semantic Web technologies,<br />Web 2.0 tools<br />
  15. 15. Twitris: Ongoing investigations<br />For a given viral “tweet” study<br />network of people who reply or re-tweet<br />do this across space and theme<br />variation of content<br />HCR – Health care reform, IE – Iran election, ISWC – Intl. Semantic Web conference<br />
  16. 16. Sparse and Dense ReTweet Graphs - Call for action vs. Information Sharing tweets<br />Showing retweet (forward chains) of a viral tweet in the HealthCare Debate data<br />“Join @MarkUdall @RitterForCO and @BennetForCO to support an up-or-down vote on the public option http://tr.im/Cm2u”<br />
  17. 17. Sparse and Dense ReTweet Graphs - Call for action vs. Information Sharing tweets<br />Showing retweet (forward chains) of a viral tweet in the Iran data<br />“Iran Election Crisis: 10 Incredible YouTube Videos http://bit.ly/vPDLo”<br />Both network and content are important in studying social dynamics (here, information diffusion)<br />
  18. 18. Analysis of Conversation and Chatter <br />Use language constructs to analyze sentiments and intentions<br />
  19. 19. Visual Analytics for Dynamic Interaction Networks<br />Model social networks as graphs<br /><ul><li>People are nodes
  20. 20. Their interactions are edges
  21. 21. Groups of connected people are communities (clusters)
  22. 22. Edge weights capture relative importance
  23. 23. Addition and deletion of edges and nodes represent changes in the network</li></ul>Challenges<br /><ul><li>Identify and localize changing portions of the network
  24. 24. Characterize the types of changes and potential causes
  25. 25. Facilitate interactive interrogation</li></li></ul><li>Events and Behaviors in Interaction Networks<br />Critical Events from a snapshot to the next<br />A cluster stays intact<br />Two clusters merge or a cluster splits<br />A new cluster forms or an existing one dissolves<br />A node joins or leaves a cluster<br />Behavioral measures (modeled as real numbers)<br />Stability and Sociability of a node<br />Influence of node on other nodes<br />Popularity of a cluster<br />
  26. 26. Paradigm: Overview, Zoom, Filter, and DoD<br />Event detection proceeds iteratively:<br />Events are computed from time Ti to time Ti+1<br />Clusters at time Ti compared with those at Ti+1 <br />Use only nodes that are active at either time which leads to significant time savings.<br />Views (map normalized measures -> colors)<br />Coarsening using multilevel hierarchies of nodes<br />Graph view, community view, event view, and node view<br />
  27. 27. Network Visualizer Architecture<br />
  28. 28. Coarse View of a Wikipedia Subgraph<br />
  29. 29. Zoom and Filter Snapshot<br />
  30. 30. Event View: Importance of Ranking<br />
  31. 31. Semantic Annotations for any resource in cyberspace (message, text, images, and AV)<br /><ul><li> Annotate resources with meaningful metadata
  32. 32. Use the metadata to process and elicit information from a given set of resources</li></li></ul><li>Automatic Semantic Annotation<br />Value-added Semagix Semantic Tagging<br />COMTEX Tagging<br />Content<br />‘Enhancement’<br />Rich Semantic <br />Metatagging<br />Limited tagging<br />(mostly syntactic)<br />Value-added<br />relevant metatags<br />added by Semagix<br />to existing <br />COMTEX tags:<br /><ul><li> Private companies
  33. 33. Type of company
  34. 34. Industry affiliation
  35. 35. Sector
  36. 36. Exchange
  37. 37. Company Execs
  38. 38. Competitors</li></ul>© Semagix, Inc.<br />
  39. 39. Annotating Sensor Data<br />
  40. 40. “NSF Playoff”<br />Embedding Metadata in <br />multimedia, a/v or sensor data <br />Video<br />Enhanced <br />Digital Cable<br />MPEG-2/4/7<br /><br />GREAT<br />USER<br />EXPERIENCE<br />MPEG<br />Encoder<br />MPEG<br />Decoder<br />Retrieve Scene Description Track<br />Create Scene Description Tree<br />License metadata decoder and <br />semantic applications to <br />device makers<br />Channel sales<br />through Video Server Vendors, Video App Servers, and Broadcasters<br />Node = AVO Object<br />Scene<br />Description<br />Tree<br />Enhanced <br />XML <br />Description<br /><ul><li>Produced by: Fox Sports  
  41. 41. Creation Date: 12/05/2000
  42. 42. League: NFL
  43. 43. Teams: Seattle Seahawks,
  44. 44. Atlanta Falcons
  45. 45. Players: John Kitna
  46. 46. Coaches: Mike Holmgren,
  47. 47. Dan Reeves
  48. 48. Location: Atlanta</li></ul>Voqutte/Taalee<br />Semantic<br />Engine<br />“NSF Playoff”<br />Node<br />Metadata-rich<br />Value-added Node<br />Object Content Information (OCI)<br />
  49. 49. Semantic Annotations for Event Visualization<br /><ul><li>Knoesis’ prior work on Semantic Event Tracking
  50. 50. Extract event information from Internet resources
  51. 51. Visualize event data through space, time, and theme</li></ul>SET<br />
  52. 52. Way Ahead: Semantic Annotations for Networks<br /><ul><li>“Richer” communication paradigm
  53. 53. Liberates parties from needing to know exact IP addresses
  54. 54. Routing based on Semantic Concepts
  55. 55. Improved efficiency
  56. 56. Single or very few traversals of network stack
  57. 57. Content is routed by the physical network based on the Semantics
  58. 58. Enhanced security and control
  59. 59. Control based on message content in addition to origin (or destination)
  60. 60. Better accountability and audit: after identifying hacking, how did Google know Chinese & Tibetan human rights advocates were targeted? Or what could attackers be after? What could they have learned?</li></li></ul><li>Semantics Aware Networking<br />
  61. 61. Way Ahead: Content Analysis to Identify Radicalization or Recruiting<br />Collaborate with linguists, social scientists, and other computer scientists to analayze social network content (Twitter, Facebook, MySpace) to weed out radical elements.<br />Alleged Fort Hood Shooter’s online post 6 months before the massacre<br />“If one suicide bomber can kill 100 enemy soldiers because they were caught off guard that would be considered a strategic victory”<br /> - Maj. NidalMalikHasan<br />
  62. 62. Way Ahead: Use and Detect misuse of Soft Power<br />Web sites in China are required to employ people who monitor and delete objectionable content; tens of thousands of others are paid to “guide” bulletin board Web exchanges in the government’s favor.<br />Use social network analysis to sense sentiments<br />Iranian reaction to Hilary Clinton’s statements on the election.<br />Impact of giving (partial) control to Pakistan to operate drones in the area<br />
  63. 63. Significant Infrastructure<br />Whole-Body Laser <br />Range Scanner<br />VERITAS<br />stereoscopic 3D <br />visualization<br />AVL<br />NMR<br />

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