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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageAmerican English
Article numbere18895
Issue number8
StatePublished - Aug 2023

Bibliographical note

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© 2023 The Authors


  • Deep learning
  • Terrorism
  • Terrorist group
  • Terrorist network


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