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

Dong Jiang; Jiajie Wu; Fangyu Ding; Tobias Ide; Jürgen Scheffran; David Helman; Shize Zhang; Yushu Qian; Jingying Fu; Shuai Chen; Xiaolan Xie; Tian Ma; Mengmeng Hao; Quansheng Ge

doi:10.1016/j.heliyon.2023.e18895

Back

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

Journal article

Open access

Peer reviewed

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

Dong Jiang, Jiajie Wu, Fangyu Ding, Tobias Ide, Jürgen Scheffran, David Helman, Shize Zhang, Yushu Qian, Jingying Fu, Shuai Chen, …

Heliyon, Vol.9(8), e18895

2023

DOI: https://doi.org/10.1016/j.heliyon.2023.e18895

Files and links (1)

pdf

Published10.13 MBDownload View

CC BY-NC-ND V4.0, Open Access

Abstract

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 deep-learning 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.

Details

Title: An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups
Authors/Creators: Dong Jiang - Institute of Geographic Sciences and Natural Resources Research
Jiajie Wu - Institute of Geographic Sciences and Natural Resources Research
Fangyu Ding - University of Chinese Academy of Sciences
Tobias Ide - Murdoch University, School of Humanities, Arts and Social Sciences
Jürgen Scheffran - Universität Hamburg
David Helman - Hebrew University of Jerusalem
Shize Zhang - Computer Network Information Center
Yushu Qian - Institute of Geographic Sciences and Natural Resources Research
Jingying Fu - University of Chinese Academy of Sciences
Shuai Chen - Institute of Geographic Sciences and Natural Resources Research
Xiaolan Xie - University of Chinese Academy of Sciences
Tian Ma - Institute of Geographic Sciences and Natural Resources Research
Mengmeng Hao - Institute of Geographic Sciences and Natural Resources Research
Quansheng Ge - Institute of Geographic Sciences and Natural Resources Research
Publication Details: Heliyon, Vol.9(8), e18895
Publisher: Elsevier Ltd.
Identifiers: 991005599066607891
Copyright: © 2023 The Authors.
Murdoch Affiliation: Centre for Biosecurity and One Health
Language: English
Resource Type: Journal article

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#16 Peace, Justice and Strong Institutions

Source: InCites

Metrics

15 File views/ downloads

135 Record Views

9 Times Cited - Web of Science

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types: Domestic collaboration; International collaboration
Citation topics: 6 Social Sciences; 6.27 Political Science; 6.27.1435 Terrorism Dynamics
Web Of Science research areas: Political Science
ESI research areas: Social Sciences, general