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Big Data and International Relations

  • Andrej Zwitter

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From November 26 to 29, 2008, ten heavily armed members of Lashkar-e-Taiba (LeT), a Kashmiri separatist group, attacked several public sites in Mumbai, India, with automatic weapons and grenades, killing 164 people and wounding three hundred. This was one of the first known instances of terrorists employing powerful search algorithms such as Twitter's or the link analysis used in Google's PageRank system, which allowed LeT members to access information from massive data pools in real-time. During the attacks, an LeT operations center based in Pakistan communicated with the terrorists via sattelite and GSM phones to provide them with open-source intelligence. From the operations center, LeT members data mined the Internet and social media, tapping into the power of Big Data to provide the attackers with an intelligence advantage over Indian law enforcement agencies. The attackers were thereby kept up to date on the status of the Indian government's response and even received personal profiles of the hostages they took in the Taj Mahal Palace hotel.

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NOTES

1 Marc Goodman, Future Crimes: Everything Is Connected, Everyone Is Vulnerable, and What We Can Do About It, ebook (New York: Knopf Doubleday Publishing Group, 2015), ch. 6.

2 Dunlap, Charles J. Jr., “The Hyper-Personalization of War: Cyber, Big Data, and the Changing Face of Conflict,” Georgetown Journal of International Affairs 15, International Engagement on Cyber IV (2014), pp. 108118 .

3 Rick Smolan and Jennifer Erwitt, The Human Face of Big Data (Sausalito, Calif.: Against All Odds Productions, 2012); Kitchin, Rob, “Big Data and Human Geography: Opportunities, Challenges and Risks,” Dialogues in Human Geography 3, no. 3 (2013), pp. 262–67; Doug Laney, “3D Data Management: Controlling Data Volume, Velocity, and Variety” (Meta Group, February 6, 2001), blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf; and Bill Vorhies, “How Many ‘V's in Big Data—The Characteristics That Define Big Data,” Business Foundation Series #2, October 31, 2013, data-magnum.com/how-many-vs-in-big-data-the-characteristics-that-define-big-data/.

4 Ibid.

5 Small chips attached to objects that contain electronically stored and wirelessly transferred information, e.g., for tracking and identifying parcels (functionally similar to QR codes).

6 Richard Winter, “Big Data: Business Opportunities, Requirements, and Oracle's Approach,” Executive Report, Winter Corporation, December 2011, p. 2, www.oracle.com/us/corporate/analystreports/infrastructure/winter-big-data-1438533.pdf.

7 Zwitter, Andrej, “Big Data Ethics,” Big Data & Society 1, no. 2 (2014), p. 2; Viktor Mayer-Schönberger and Kenneth Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think (Boston: Houghton Mifflin Harcourt, 2013), p. 52ff.

8 Mayer-Schönberger and Cukier, Big Data, pp. 26–31.

9 Zwitter, “Big Data Ethics,” p. 2.

10 Kitchin, Rob, “Big Data, New Epistemologies and Paradigm Shifts,” Big Data & Society 1, no. 1 (2014), p. 2.

11 Ray Wang, “Monday's Musings: Beyond The Three V's of Big Data—Viscosity and Virality,” Forbes, February 27, 2012.

12 Weng, Lilian, Menczer, Filippo, and Ahn, Yong-Yeol, “Virality Prediction and Community Structure in Social Networks,” Scientific Reports 3, Article number: 2522 (published online August 28, 2013).

13 Jacob Silverman, “Time to Regulate Data Brokers,” Al Jazeera America, “Opinion” section, January 23, 2014, america.aljazeera.com/opinions/2014/1/time-to-regulatedatabrokers.html.

14 Goodman, Future Crimes, ch. 4.

15 Lawrence Lessig, Code and Other Laws of Cyberspace (New York: Basic Books, 1999).

16 Chris Anderson, “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete,” WIRED, June 23, 2008, archive.wired.com/science/discoveries/magazine/16-07/pb_theory/.

17 F. Johansson et al., “Detecting Emergent Conflicts through Web Mining and Visualization,” Intelligence and Security Informatics Conference (EISIC), 2011 European, pp. 346–53.

18 Lazer, David et al. , “The Parable of Google Flu: Traps in Big Data Analysis,” Science 343, no. 6176 (2014), pp. 1203–205.

19 “Ushahidi,” www.ushahidi.com/, accessed April 16, 2015; and “Kenyan Elections 2013, Community Wiki—Ushahidi,” wiki.ushahidi.com/display/WIKI/Uchaguzi+-+Kenyan+Elections+2013, accessed April 16, 2015.

20 Center for Social Media, “2014 Social Media Survey Results,” Annual Survey (Alexandria, Va.: International Association of Chiefs of Police, Fall 2014), www.iacpsocialmedia.org/Resources/Publications/2014SurveyResults.aspx.

21 UN Secretary-General, “Information and Communications Technology in the United Nations,” Report of the Secretary-General to the General Assembly, October 10, 2014, UN document A/69/517, para. 40.

22 John Karlsrud, “Peacekeeping 4.0: Harnessing the Potential of Big Data, Social Media, and Cyber Technologies,” in Jan-Frederik Kremer and Benedikt Müller, eds., Cyberspace and International Relations: Theory, Prospects and Challenges, 2014 edition (Heidelberg, Ger.: Springer, 2013), pp. 141–60.

23 Satellite Sentinel Project, “Evidence of Burial of Human Remains in Kadugli, South Kordofan,” Special Report, Harvard Humanitarian Initiative, August 24, 2011, hhi.harvard.edu/publications/special-report-evidence-burial-human-remains-kadugli-south-kordofan.

24 Sheldon Himelfarb, “Can Big Data Stop Wars Before They Happen?” Foreign Policy, April 25, 2014, foreignpolicy.com/2014/04/25/can-big-data-stop-wars-before-they-happen/.

25 Rasool Dawar (Associated Press), “Taliban Threatens Foreign Aid Workers,” Washingtion Times online, August 26, 2010, www.washingtontimes.com/news/2010/aug/26/taliban-threatens-foreign-aid-workers/.

26 Marc Parry, “Academics Join Relief Efforts Around the World as Crisis Mappers,” Chronicle of Higher Education, March 27, 2011, chronicle.com/article/Academics-Join-Relief-Efforts/126912/.

27 Mireille Hildebrandt, Smart Technologies and the End(s) of Law: Novel Entanglements of Law and Technology (Cheltenham, U.K.: Edward Elgar Pub, 2015), p. 90.

28 John J. Reilly Center of the University of Notre Dame, “Predictive Policing,” reilly.nd.edu/outreach/emerging-ethical-dilemmas-and-policy-issues-in-science-and-technology-2014/predictive-policing/.

29 I.e., a website that cannot be indexed by search engines and to which one only has access through identity-cloaking protocols such as Tor.

30 “Europe-v-Facebook.org,” www.europe-v-facebook.org, accessed August 28, 2015.

31 Reuters, “Google Accused of ‘Passive-Aggressiveness’ over EU Right to Be Forgotten,” Telegraph, “Technology” section, November 5, 2014, www.telegraph.co.uk/technology/google/11210836/Google-accused-of-passive-aggressiveness-over-EU-right-to-be-forgotten.html.

32 “Global Data Protection Handbook,” dlapiperdataprotection.com/#handbook/world-map-section/c1_AR/c2_EG, accessed April 16, 2015.

33 Jan-Frederik Kremer and Benedikt Müller, eds., Cyberspace and International Relations: Theory, Prospects and Challenges, 2014 edition (Heidelberg, Ger.: Springer, 2013), p. vii.

* This essay was produced as part of the cooperation between the Austrian Institute for International Affairs and the Danube University Krems, Austria.

Big Data and International Relations

  • Andrej Zwitter

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