U.S. flag

An official website of the United States government, Department of Justice.

NCJRS Virtual Library

The Virtual Library houses over 235,000 criminal justice resources, including all known OJP works.
Click here to search the NCJRS Virtual Library

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

NCJ Number
Benjamin W.K. Hung; Shashika R. Muramudalige; Anura P. Jayasumana; Jytte Klausen; Rosanne Libretti ; Evan Moloney; Priyanka Renugopalakrishnan
Date Published
7 pages

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


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. The current 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. (Publisher Abstract Provided)