Terrorism informatics : knowledge management and data mining for homeland security

Terrorism informatics has been defined as the application of advanced methodologies, information fusion and analysis techniques to acquire, integrate process, analyze, and manage the diversity of terrorism-related information for international and homeland security-related applications. The variety of methods used in terrorism informatics are derived from Computer Science, Informatics, Statistics, Mathematics, Linguistics, Social Sciences, and Public Policy and they involve the collection of a mass of information from multiple sources and in numerous languages.

TERRORISM INFORMATICS: Knowledge Management and Data Mining for Homeland Security will provide an interdisciplinary and comprehensive survey of the state-of-the-art of terrorism informatics domain along three basic dimensions: methodological issues in terrorism research; information infusion techniques to support terrorism prevention, detection, and response; and legal, social, privacy, and data confidentiality challenges and approaches.

• Preface p. xv
• Editor Biographies p. xix
• Author Biographies p. xxv
• Unit I Methodological Issues In Terrorism Research
• Chapter 1 Domain Mapping of Contemporary Terrorism Research Edna Reid and Hsinchun Chen
• Chapter Overview p. 3
• 1 Introduction p. 4
• 2 Related Work p. 4
• 3 Research Design p. 8
• 4 Results p. 11
• 5 Conclusion p. 20
• 6 Acknowledgements p. 21
• References p. 21
• Appendix: List of 42 Influential Terrorism Researchers p. 23
• Suggested Readings p. 26
• Online Resources p. 26
• Discussion Questions p. 26
• Chapter 2 Research on Terrorism: A Review of the Impact of 9/11 and the Global War on Terrorism Andrew Silke
• Chapter Overview p. 27
• 1 Introduction p. 28
• 2 The Nature of This Review p. 32
• 3 Trends in Data-Gathering and Analysis p. 33
• 4 Research on Terrorist Groups p. 38
• 5 Research on Terrorist Tactics p. 41
• 6 Some Conceptual Issues p. 44
• 7 Conclusions p. 46
• References p. 48
• Suggested Readings p. 49
• Discussion Questions p. 49
• Chapter 3 Who Are the Key Figures in ""Terrorism Studies""? Sam Raphael
• Chapter Overview  p. 51
• 1 Introduction p. 52
• 2 Constructing the Framework: The Multiplicity of ""Terrorism Studies,"" the Role of the Audience, and the Importance of Methdological Rigor p. 53
• 3 Employing the Framework: Selecting the Time Period; Constructing the Research Community p. 56
• 4 Constructing the Audience p. 59
• 5 Measuring the Opinions of the Relevant Audience: What Does the Peer Research Community Think? p. 60
• Establishing the Threshold: Exactly What Determines an Expert as Key? p. 62
• Conclusion p. 67
• References and Footnotes p. 69
• Suggested Readings p. 72
• Discussion Questions p. 72
• Chapter 4 Interviewing Terrorists: A Case for Primary Research John Horgan
• Chapter Overview p. 73
• 1 Introduction p. 74
• 2 Procuring Interviews p. 75
• 3 A Case Illustration: Interviews with the IRA p. 79
• 4 Finding and ""Collecting"" Participants p. 80
• 5 A Case Example: Interviewing a Terrorist p. 85
• 6 Interview Considerations p. 89
• 7 Issues of Validity and Reliability p. 94
• 8 Conclusions p. 96
• Post-Script p. 97
• References p. 97
• Suggested Readings p. 99
• Discussion Questions p. 99
• Chapter 5 Resolving a Terrorist Insurgency by Addressing Its Root Causes Joshua Sinai
• 1 Introduction p. 102
• 2 Why Root Causes Are Significant p. 103
• 3 How to Resolve a Conflict's Root Causes p. 106
• Conclusion p. 111
• References and Footnotes p. 112
• Suggested Readings p. 114
• Online Resources p. 114
• Discussion Questions p. 114
• Chapter 6 A Quantitative Analysis of ""Root Causes of Conflict"" p. 115 Mihaela Bobeica and Jean-Paul Jéral and Teofilo Garcia and Clive Best Chapter Overview
• 1 Introduction p. 116
• 2 Conflict Indicators and Automatic Data Analysis for Early Warning p. 117
• 3 LSA Applied to English Articles in EMM p. 120
• 4 Results and Discussion p. 124
• 5 Conclusions and Future Work p. 131
• References p. 132
• Suggested Readings p. 133
• Online Resources p. 133
• Discussion Questions p. 133
• Appendix 1 Tables p. 134
• Appendix 2 Conflict Indicators p. 136
• Appendix 3 Figures p. 137
• Appendix 4 Word Lists p. 139
• Chapter 7 Countering Terrorism with Knowledge James O. Ellis III
• Chapter Overview p. 141
• 1 Introduction p. 142
• 2 Problems in Researching Terrorism p. 142
• 3 Problems in Terrorism Research p. 144
• 4 Problems in Terrorism Databases p. 145
• 5 MIPT as an Information Clearinghouse p. 147
• 6 MIPT-Funded Terrorism Databases p. 149
• 7 MIPT Terrorism Knowledge Base p. 150
• 8 Better Knowing What We Know about Terrorism p. 151
• 9 Acknowledgements p. 153
• References p. 153
• Suggested Readings p. 154
• Online Resources p. 154
• Discussion Questions p. 155
• Chapter 8 Toward a Target-specific Method of Threat Assessment Yael Shahar
• Chapter Overview p. 157
• 1 Introduction p. 158
• 2 Methodology p. 158
• 3 Organization-specific Indicators p. 160
• 4 Synthesis p. 169
• 5 Summary: Scenarios Most Likely to Be Carried out by Relevant Terrorist Groups p. 171
• 6 Conclusion p. 172
• Suggested Readings p. 173
• Online Resources p. 174
• Discussion Questions p. 174
• Chapter 9 Identifying and Exploiting Group Learning Patterns for Counterterrorism Horacio R. Trujillo and Brian A. Jackson
• Chapter Overview p. 175
• 1 Introduction p. 176
• 2 Organizational Learning p. 177
• 3 A Four Stage Model of Organizational Learning p. 178
• 4 Paths of Organizational Learning p. 182
• 5 Determinants of Organizational Learning p. 183
• 6 How an Organizational Learning Model Can Inform the Design of Terrorism Informatics Systems p. 186
• 7 Conclusions and Discussion p. 190
• 8 Acknowledgements p. 191
• Notes p. 192
• References p. 194
• Suggested Readings p. 195
• Online Resources p. 195
• Discussion Questions p. 195
• Chapter 10 Homeland Insecurity: Data Mining, Privacy, Disclosure Limitation, and the Hunt for Terrorists Stephen E. Fienberg
• Chapter Overview p. 197
• 1 Introduction p. 198
• 2 Homeland Security and the Search for Terrorists p. 200
• 3 Matching and Record Linkage Methods p. 203
• 4 Encryption, Multi-party Computation, and Privacy-preserving Datamining p. 205
• 5 Selective Revelation, Risk-utility Tradeoff, and Disclosure Limitation Assessment p. 207
• 6 Analyzing Network Data Based on Transactions p. 210
• 7 Conclusions p. 212
• 8 Acknowledgments p. 214
• Notes p. 214
• References p. 215
• Suggested Readings p. 217
• Discussion Questions p. 218
• Unit II Terrorism Informatics To Support Prevention, Detection, And Response
• Chapter 11 Case Study of Jihad on the Web: A Web Mining Approach Hsinchun Chen and Jialun Qin and Edna Reid and Yilu Zhou and Marc Sageman
• Chapter Overview p. 221
• 1 Introduction p. 222
• 2 Previous Research p. 222
• 3 Proposed Approach p. 225
• 4 Analysis Results p. 228
• 5 Discussion and Future Work p. 233
• References p. 234
• Suggested Readings p. 235
• Online Resources p. 235
• Discussion Questions p. 235
• Chapter 12 Studying Global Extremist Organizations' Internet Presence Using the Dark Web Attribute System: A Three Region Comparison Study Jialun Qin and Yilu Zhou and Edna Reid and Hsinchun Chen
• Chapter Overview p. 237
• 1 Introduction p. 238
• 2 Literature Review p. 239
• 3 Studying Global Extremist Organizations' Internet Usage: A Three-Region Empirical Study p. 243
• 4 Conclusions and Future Directions p. 261
• References p. 263
• Suggested Readings p. 265
• Online Resources p. 265
• Discussion Questions p. 266
• Chapter 13 Content Analysis of Jihadi Extremist Groups' Videos Arab Salem and Edna Reid and Hsinchun Chen
• Chapter Overview  p. 267
• 1 Introduction p. 268
• 2 Related Work p. 269
• 3 Methodology p. 272
• 4 Results p. 276
• 5 Conclusion p. 280
• 6 Acknowledgements p. 281
• References p. 281
• Suggested Readings p. 283
• Online Resources p. 283
• Discussion Questions p. 284
• Chapter 14 Analysis of Affect Intensities in Extremist Group Forums Ahmed Abbasi and Hsinchun Chen
• Chapter Overview  p. 285
• 1 Introduction p. 286
• 2 Related Work p. 287
• 3 Research Gaps and Questions p. 292
• 4 Research Questions p. 293
• 5 Research Design p. 294
• 6 System Design p. 294
• 7 Evaluation p. 297
• 8 Results p. 300
• 9 Conclusions p. 303
• References p. 304
• Suggested Readings p. 306
• Online Resources p. 306
• Discussion Questions p. 307
• Chapter 15 Document Selection for Extracting Entity and Relationship Instances of Terrorist Events Zhen Sun and Ee-Peng Lim and Kuiyu Chang and Maggy Anastasia Suryanto and Rohan Kumar Gunaratna
• Chapter Overview  p. 309
• 1 Introduction p. 310
• 2 Literature Review p. 313
• 3 Domain Specific Event Entity Relation Extraction Task with Document Ranking p. 317
• 4 Case Studies p. 325
• 5 Conclusions and Discussion p. 343
• 6 Acknowledgements p. 344
• References p. 344
• Suggested Readings p. 346
• Online Resources p. 346
• Discussion Questions p. 346
• Chapter 16 Data Distortion Methods and Metrics in a Terrorist Analysis System Shuting Xu and Jun Zhang
• Chapter Overview  p. 347
• 1 Introduction p. 348
• 2 Terrorist Analysis System p. 350
• 3 Data Distortion p. 350
• 4 Data Distortion Measures p. 354
• 5 Utility Measure p. 357
• 6 Experiments and Results p. 358
• 7 Conclusions and Discussions p. 361
• References p. 362
• Suggested Readings p. 364
• Online Resources p. 364
• Discussion Questions p. 364
• Chapter 17 Content-Based Detection of Terrorists Browsing the Web Using an Advanced Terror Detection System (ATDS) Yuval Elovici and Bracha Shapira and Mark Last and Omer Zaafrany and Menahem Friedman and Moti Schneider and Abraham Kandel
• Chapter Overview  p. 365
• 1 Introduction p. 366
• 2 Related Work p. 367
• 3 Advanced Terrorist Detection System p. 368
• 4 Evaluation p. 374
• 5 Conclusions and Discussion p. 381
• 6 Acknowledgements p. 382
• References p. 382
• Suggested Readings p. 384
• Online Resources p. 384
• Discussion Questions p. 384
• Chapter 18 Text Mining the Biomedical Literature for Identification of Potential Virus/Bacterium as Bio-terrorism Weapons Xiaohua Hu and Xiaodan Zhang and Daniel Wu and Xiaohua Zhou and Peter Rumm
• Chapter Overview  p. 385
• 1 Introduction p. 386
• 2 Related Works p. 387
• 3 Background of Virus and Bacterium p. 387
• 4 Method p. 389
• 5 Experimental Results p. 394
• 6 Potential Significance for Public Health and Homeland Security p. 404
• 7 Acknowledgements p. 404
• References p. 404
• Suggested Readings p. 405
• Online Resources p. 405
• Discussion Questions p. 405
• Chapter 19 Leveraging One-Class SVM and Semantic Analysis to Detect Anomalous Content Ozgur Yilmazel and Svetlana Symonenko and Niranjan Balasubramanian and Elizabeth D. Liddy
• Chapter Overview  p. 407
• 1 Introduction p. 408
• 2 Overview of Related Work p. 409
• 3 Case Study: One-class Categorization Approach to the Problem of Identifying Anomalous Content p. 411
• 4 Conclusions and Discussion p. 420
• References p. 421
• Acknowledgements p. 423
• Suggested Readings p. 423
• Online Resources p. 423
• Discussion Questions p. 423
• Chapter 20 Individual and Collective Analysis of Anomalies in Message Traffic D.B. Skillicorn
• Chapter Overview  p. 425
• 1 Introduction p. 426
• 2 Analysis of Single Messages p. 428
• 3 Analysis of Multiple Messages p. 435
• 4 Conclusions p. 447
• 5 Acknowledgements p. 448
• References p. 448
• Suggested Readings p. 449
• Discussion Questions p. 449
• Chapter 21 Addressing Insider Threat through Cost-Sensitive Document Classification Young-Woo Seo and Katia Sycara
• Chapter Overview  p. 451
• 1 Introduction p. 452
• 2 Related Work p. 454
• 3 Overview p. 455
• 4 Classification for Confidential Authorization p. 457
• 5 Experiments p. 461
• 6 Conclusion p. 468
• 7 Acknowledgments p. 470
• References p. 470
• Suggested Readings p. 471
• Online Resources p. 472
• Discussion Questions p. 472
• Chapter 22 Using Web Mining and Social Network Analysis to Study the Emergence of Cyber Communities in Blogs Michael Chau and Jennifer Xu
• Chapter Overview p. 473
• 1 Introduction p. 474
• 2 Research Background p. 475
• 3 Research Questions p. 479
• 4 A Framework for Blog Collection and Analysis p. 480
• 5 A Case Study on Xanga p. 483
• 6 Conclusion and Future Directions p. 488
• 7 Acknowledgements p. 489
• References p. 489
• Appendix p. 492
• Suggested Readings p. 493
• Online Resources p. 493
• Discussion Questions p. 493
• Chapter 23 Automatic Extraction of Deceptive Behavioral Cues from Video Thomas O. Meservy and Matthew L. Jensen and W. John Kruse and Judee K. Burgoon and Jay F. Nunamaker Jr.
• Chapter Overview p. 495
• 1 Introduction p. 496
• 2 Literature Review p. 496
• 3 Research Method and Examples p. 501
• 4 Experiments and Results p. 510
• 5 Conclusion p. 512
• 6 Acknowledgements p. 513
• References p. 513
• Suggested Readings p. 515
• Online Resources p. 515
• Discussion Questions p. 516
• Chapter 24 Situational Awareness Technologies for Disaster Response Naveen Ashish and Ronald Eguchi and Rajesh Hegde and Charles Huyck and Dmitri Kalashnikov and Sharad Mehrotra and Padhraic Smyth and Nalini Venkatasubramanian
• Chapter Overview p. 517
• 1 Introduction p. 518
• 2 Situational Awareness p. 523
• 3 Event Extraction p. 527
• 4 Event Data Management p. 531
• 5 Event Analysis and Visualization p. 534
• 6 Artifacts p. 539
• 7 Conclusion p. 541
• 8 Acknowledgements p. 542
• References p. 543
• Online Resources p. 544
• Questions for Discussion p. 544
• Author Index p. 545
• Subject Index p. 547