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" Understanding the Dynamics of Unclaimed Terrorism Events in Pakistan: A Machine Learning Approach "
Christie, Evan
Asif Nawaz, Muhammad
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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1053368
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| Doc. No
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TL52485
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| Main Entry
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Christie, Evan
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| Title & Author
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Understanding the Dynamics of Unclaimed Terrorism Events in Pakistan: A Machine Learning Approach\ Christie, EvanAsif Nawaz, Muhammad
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| College
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The University of Maine
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| Date
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2019
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| Degree
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M.A.
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2019
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| Note
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55 p.
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| Abstract
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Terrorists thrive on media coverage because it multiplies the effect of an attack (Nacos, 2007). However, according to the Global Terrorism Database (GTD), only ten percent of terrorist attacks have been attributed globally from 1970 to 2017 (START, 2017). If the media coverage is a prerequisite for a terrorist group’s survival, the lack of attributed attacks in the world is puzzling. This thesis examines the phenomenon of unattributed terrorist attacks using Pakistan as a case study. Pakistan is used as a case study because the percentage of claimed terrorist attacks in Pakistan closely resembles the global average of the lack of attribution of terrorist attacks – only fifteen percent of attacks are attributed in Pakistan. By using different organizational attributes – like attack, target, weapon preferences, spatial attack data, and lethality of attacks, this study attempts to match unattributed terror attacks to known groups.
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| Descriptor
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Political science
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| Added Entry
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Asif Nawaz, Muhammad
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| Added Entry
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The University of Maine
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