Recognizing Radicalization Indicators in Text Documents Using Human-in-the-Loop Information Extraction and NLP Techniques

Among the operational shortfalls that hinder law enforcement from achieving greater success in preventing terrorist attacks is the difficulty in dynamically assessing individualized violent extremism risk at scale given the enormous amount of primarily text-based records in disparate databases. In this work, we undertake the critical task of employing natural language processing (NLP) techniques and supervised machine learning models to classify textual data in analyst and investigator notes and reports for radicalization behavioral indicators. This effort to generate structured knowledge will build towards an operational capability to assist analysts in rapidly mining law enforcement and intelligence databases for cues and risk indicators. In the near-term, this effort also enables more rapid coding of biographical radicalization profiles to augment a research database of violent extremists and their exhibited behavioral indicators.

• Publication year

  2019

• Venue

  IEEE International Conference on Technologies for Homeland Security

• Publication date

  2019-11-01

• Fields of study

  Law, Computer Science, Political Science

• Identifiers
  DOI 10.1109/HST47167.2019.9032956
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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