Understanding the Dynamics of Unclaimed Terrorism Events in Pakistan: A Machine Learning Approach
نام عام مواد
[Thesis]
نام نخستين پديدآور
Christie, Evan
نام ساير پديدآوران
Asif Nawaz, Muhammad
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
The University of Maine
تاریخ نشرو بخش و غیره
2019
يادداشت کلی
متن يادداشت
55 p.
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
M.A.
کسي که مدرک را اعطا کرده
The University of Maine
امتياز متن
2019
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
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.
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Political science
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )