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

• NCJ Number: 304905
• Date Published: 2019
• Author(s): Benjamin W.K. Hung; Shashika R. Muramudalige; Anura P. Jayasumana; Jytte Klausen; Rosanne Libretti; Evan Moloney; Priyanka Renugopalakrishnan
• Length: 7 pages
• Grant Number(s): 2013-ZA-BX-0005
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)

Annotation

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

Abstract

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)