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

1. Enhancing Soft Power (ESP) Analysis and Situational Awareness over network of Computers, Sensors, People, and Content for enhancing Soft Power AmitSheth, LexisNexis Eminent Scholar Director, Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) Wright State University http://knoesis.org

2. “…challenge accepted paradigms to propose new ways of fighting from air, space, and cyberspace.” - General Stephen Lorenz

3. A General who led recent war “It’s not the number of people you kill, it’s the number of people you convince.”

4. Network Analysis & Visualization Consider all challenges that can be addressed by advanced semantic analytics and visualization in the cyberspace Cyberspace = {computers/networks, sensors, social <people, conversations>} For issues effecting security in cyberspace and to enhancing military’s capacity to project soft power So broadly, People-Content-NetworkAnalysis and Visualization

5. Institutions Involved Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), UCI (Ramesh Jain), OSU Faculty in Visual Analytics [Srini Parthasarthy] WSU Faculty in Internet/WWW, Semantic Web, Sensor Web, Social Computing, Network Security [Bin Wang] , Visualization [Tom Wischgoll], and Cognitive Science [John Flach,…] Optionally, faculty at Indiana University Center for Complex Networks & Systems, and Purdue University Preliminary thoughts on COE on Enhancing Soft Power (ESP)

6. Networks in Cyberspace Networks in Cyber Space Sensor Networks Computer Networks Social Networks

7. Networks and Cyber Security Networks in Cyber Space Cyber Security Sensor Networks Computer Networks Social Networks

8. Networks and Soft Power Networks in Cyber Space Computer Networks Sensor Networks Soft Power Citizen Sensing Social Networks

9. Hard Power – “Carrots and Sticks”

10. Soft Power: “The Second Face of Power” “Affect behavior through attraction” “Soft power is attractive power” Also called cooptive power: the ability to shape the preferences of others getting others to want the outcomes that we want

11. How important is soft power? Henry Crumpton– CIA covert-op leader Hard power – 25% Soft power – 75% Wield soft power (to counter terror groups in Iraq and Afghanistan) through Empowerment of native people Developmental and educational projects Measure impact of policy alternatives development initiatives

12. Using soft power and monitoring its effects One approach – Analyze online social networks Kno.e.sis Ohio State Aggregate social perceptions Analyze with a focus theme Use spatio-temporal aspects Provide sentiment, intension, cultural analysis Visualize the interaction graphs formed in social networks (more later) Twitris (live demo) How hard is this?

13. Twitris: Architecture

14. Twitris: Features and Research Issues Data intensive Processing and storage of data Vs meta data Timeliness Live resource aggregation Near real time Tunable spatio-temporal scope/weights Global Iran election Vs. Local health care debate Parallel programming, cloud computing (map-reduce) Information retrieval, natural language processing (harder with casual text), machine learning Semantic Web technologies, Web 2.0 tools

15. Twitris: Ongoing investigations For a given viral “tweet” study network of people who reply or re-tweet do this across space and theme variation of content HCR – Health care reform, IE – Iran election, ISWC – Intl. Semantic Web conference

16. Sparse and Dense ReTweet Graphs - Call for action vs. Information Sharing tweets Showing retweet (forward chains) of a viral tweet in the HealthCare Debate data “Join @MarkUdall @RitterForCO and @BennetForCO to support an up-or-down vote on the public option http://tr.im/Cm2u”

17. Sparse and Dense ReTweet Graphs - Call for action vs. Information Sharing tweets Showing retweet (forward chains) of a viral tweet in the Iran data “Iran Election Crisis: 10 Incredible YouTube Videos http://bit.ly/vPDLo” Both network and content are important in studying social dynamics (here, information diffusion)

18. Analysis of Conversation and Chatter Use language constructs to analyze sentiments and intentions

20. Their interactions are edges

21. Groups of connected people are communities (clusters)

22. Edge weights capture relative importance

24. Characterize the types of changes and potential causes

26. Paradigm: Overview, Zoom, Filter, and DoD Event detection proceeds iteratively: Events are computed from time Ti to time Ti+1 Clusters at time Ti compared with those at Ti+1 Use only nodes that are active at either time which leads to significant time savings. Views (map normalized measures -> colors) Coarsening using multilevel hierarchies of nodes Graph view, community view, event view, and node view

27. Network Visualizer Architecture

28. Coarse View of a Wikipedia Subgraph

29. Zoom and Filter Snapshot

30. Event View: Importance of Ranking

33. Type of company

34. Industry affiliation

35. Sector

36. Exchange

37. Company Execs

38. Competitors© Semagix, Inc.

39. Annotating Sensor Data

41. Creation Date: 12/05/2000

42. League: NFL

43. Teams: Seattle Seahawks,

44. Atlanta Falcons

45. Players: John Kitna

46. Coaches: Mike Holmgren,

47. Dan Reeves

48. Location: AtlantaVoqutte/Taalee Semantic Engine “NSF Playoff” Node Metadata-rich Value-added Node Object Content Information (OCI)

50. Extract event information from Internet resources

51. Visualize event data through space, time, and themeSET On the Web: Semantic Event Tracker

52. Trusted Active Perception Abstracting high level understanding (perception) from low level sensor (SIGINT) data from heterogeneous sensors E.g., recognizing a storm event in an area of interest at the time of interest from variety of hundred to thousands of heterogeneous sensor data Trusted Perception Cycle demo

54. Liberates parties from needing to know exact IP addresses

55. Routing based on Semantic Concepts

56. Improved efficiency

57. Single or very few traversals of network stack

58. Content is routed by the physical network based on the Semantics

59. Enhanced security and control

60. Control based on message content in addition to origin (or destination)

62. Way Ahead: Content Analysis to Identify Radicalization or Recruiting Collaborate with linguists, social scientists, and other computer scientists to analayze social network content (Twitter, Facebook, MySpace) to weed out radical elements. Alleged Fort Hood Shooter’s online post 6 months before the massacre “If one suicide bomber can kill 100 enemy soldiers because they were caught off guard that would be considered a strategic victory” - Maj. NidalMalikHasan

63. Way Ahead: Use and Detect misuse of Soft Power Web sites in a major world power 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. Use social network analysis to sense sentiments Iranian reaction to Hilary Clinton’s statements on the election. Impact of giving (partial) control to an “ally” to operate drones in the area

64. Significant Infrastructure Whole-Body Laser Range Scanner VERITAS stereoscopic 3D visualization AVL NMR