Understanding Patterns of Terrorism in India (2007-2017) Using Artificial Intelligence Machine Learning

Dinesh Verma, Rithvik Yarlagadda, Scott Sigmund Gartner, Diane Felmlee

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


With the tremendous increases in Artificial Intelligence (AI) computing technology capabilities, applications of AI approaches to terrorist data can yield useful insights into the interaction of terrorists, governance, and geography. There have been few applications of machine learning techniques to understand patterns of terrorist behavior. Specifically, little work has been done to analyze terrorism patterns in India, which experiences one of the world's highest levels of terrorism. We apply "shallow AI models" to a decade of terrorist incidents in India. We show that AI approaches generate highly accurate models that predict levels of violent incident behavior across locations from a history of past attacks, and identify the principal factors correlated with a location being targeted. This study provides an example of socially-relevant AI research, expands our understanding of the dynamics of terrorism in a way that can help to shape counterterrorism policy and contributes to our greater recognition of the interwoven relationship of technology, knowledge, and society.

Original languageEnglish (US)
Pages (from-to)23-39
Number of pages17
JournalInternational Journal of Technology, Knowledge and Society
Issue number4
StatePublished - 2019

All Science Journal Classification (ASJC) codes

  • Education
  • Social Sciences (miscellaneous)
  • Library and Information Sciences
  • Computer Science Applications
  • Engineering (miscellaneous)


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