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Counterterrorism Mining for Individuals Semantically-Similar to Watchlist Members

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Counterterrorism and Open Source Intelligence

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

A key counterterrorism problem is how to identify people that should be added to a watchlist even though they have no direct communication with its members. One of the main ways a watchlist is expanded is by monitoring the emergence of new persons who establish contact with those on the list. Unfortunately, this severely limits the time horizon for managing risks of dark network behaviors because individuals are already actively involved with one another and more likely to be discussing and planning terrorist actions. In contrast, a wider time horizon results from identifying individuals who do not yet have communication with watchlist members, while they have highly similar semantic networks. Discussion forums are considered a primary source of intelligence about plans for dark behaviors. The research reported here develops a method for locating individuals in discussion forums who have highly similar semantic networks to some reference network, either based on watchlist members’ observed message content or based on other standards such as radical jihadists’ semantic networks extracted from messages they disseminate on the internet. This research demonstrates such methods using a Pakistani discussion forum with diverse content. Of those pairs of individuals with highly-similar semantic networks, 61% have no direct contact in the forum. It is likely that adding to watchlists individuals who have a high match to a reference semantic network lengthens the time horizon for identifying high risk dark behaviors.

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Acknowledgements

I am grateful for programming support from Rafal Radulski, Mike Hutcheson, and Brittany Johnson at the University of Illinois at Chicago, to Jonathan Chang, computer scientist at Facebook and Princeton for help in executing his LDA in R code, to Michael W. Berry for resolving some computational problems with a Matlab pLSI script, and to Gabriel Weimann, University of Haifa for providing translated radical jihadist texts in response to Obama’s June 4, 2008 Cairo speech. The version 3.0 WORDij software used in this research was supported in part by the National Science Foundation’s Human and Social Dynamics (HSD) Award #SES-527487.

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  1. Department of Communication, University of Illinois at Chicago, Chicago, IL, USA

    James A. Danowski

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Correspondence to James A. Danowski .

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  1. The Maersk McKinney Moller Institute, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark

    Uffe Kock Wiil

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Danowski, J.A. (2011). Counterterrorism Mining for Individuals Semantically-Similar to Watchlist Members. In: Wiil, U.K. (eds) Counterterrorism and Open Source Intelligence. Lecture Notes in Social Networks. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0388-3_12

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  • DOI: https://doi.org/10.1007/978-3-7091-0388-3_12

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