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                            Library & Multimedia
 Recognizing Radicalization Indicators in Text Documents Using Human-in-the-Loop Information Extraction and NLP Techniques
NCJ Number
              304905

Author(s)
          Benjamin W.K. Hung;
                        Shashika R. Muramudalige;
                        Anura P. Jayasumana;
                        Jytte Klausen;
                        Rosanne Libretti;
                        Evan Moloney;
                        Priyanka Renugopalakrishnan
Date Published

2019
Length
              7 pages
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)
      
  
          Additional Details
Grant Number(s)
        2013-ZA-BX-0005


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Prisoner Recollections: The Role of Internet Use and Real-Life Networks in the Early Radicalization of Islamist Terrorist Offenders
    
  
  
Date Published: January 1, 2019
    
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        Counter-terrorism intelligence
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