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An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups

文献类型：期刊论文

作者Jiang, Dong1,2; Wu, Jiajie1,2; Ding, Fangyu1,2; Ide, Tobias3; Scheffran, Juergen4; Helman, David5,6; Zhang, Shize7; Qian, Yushu1; Fu, Jingying1,2; Chen, Shuai1,2
刊名HELIYON
出版日期2023-08-01
卷号9期号:8页码:17
关键词Terrorism Deep learning Terrorist group Terrorist network
DOI10.1016/j.heliyon.2023.e18895
通讯作者Hao, Mengmeng(haomm@igsnrr.ac.cn) ; Ge, Quansheng(geqs@igsnrr.ac.cn)
英文摘要Human security is threatened by terrorism in the 21st century. A rapidly growing field of study aims to understand terrorist attack patterns for counter-terrorism policies. Existing research aimed at predicting terrorism from a single perspective, typically employing only background contextual information or past attacks of terrorist groups, has reached its limits. Here, we propose an integrated deeplearning framework that incorporates the background context of past attacked locations, social networks, and past actions of individual terrorist groups to discover the behavior patterns of terrorist groups. The results show that our framework outperforms the conventional base model at different spatio-temporal resolutions. Further, our model can project future targets of active terrorist groups to identify high-risk areas and offer other attack-related information in sequence for a specific terrorist group. Our findings highlight that the combination of a deep learning approach and multi-scalar data can provide groundbreaking insights into terrorism and other organized violent crimes.
WOS关键词NETWORKS ; EXTREMISM ; VIOLENCE ; TRENDS ; GAME
资助项目National Natural Science Foundation of China[42001238] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040305]
WOS研究方向Science & Technology - Other Topics
语种英语
出版者CELL PRESS
WOS记录号WOS:001059495300001
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/196930]  
专题中国科学院地理科学与资源研究所
通讯作者Hao, Mengmeng; Ge, Quansheng
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Murdoch Univ, Harry Butler Inst, Ctr Biosecur & One Hlth, Perth, WA 6150, Australia
4.Univ Hamburg, Inst Geog, Ctr Earth Syst Res & Sustainabil, D-20144 Hamburg, Germany
5.Hebrew Univ Jerusalem, Inst Environm Sci, Robert H Smith Fac Agr Food & Environm, Dept Soil & Water Sci, IL-7610001 Rehovot, Israel
6.Hebrew Univ Jerusalem, Adv Sch Environm Studies, Jerusalem, Israel
7.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Jiang, Dong,Wu, Jiajie,Ding, Fangyu,et al. An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups[J]. HELIYON,2023,9(8):17.
APA Jiang, Dong.,Wu, Jiajie.,Ding, Fangyu.,Ide, Tobias.,Scheffran, Juergen.,...&Ge, Quansheng.(2023).An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups.HELIYON,9(8),17.
MLA Jiang, Dong,et al."An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups".HELIYON 9.8(2023):17.

入库方式： OAI收割

来源：地理科学与资源研究所

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