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Finding Emergent Patterns of Behaviors in Dynamic Heterogeneous Social and Behavioral Data: Experience with Violent Extremist Radicalization Trajectories

Finding Emergent Patterns of Behaviors in Dynamic Heterogeneous Social and Behavioral Data: Experience with Violent Extremist Radicalization Trajectories

Speaker: Anura Jayasumana – Fort Collins, CO, United States
Topic(s): Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science

Abstract

The search for individuals or entities undertaking latent or emergent behaviors has applicability in the areas of homeland security, consumer analytics, behavioral health, and cybersecurity. In this setting, even partial matches to hypothesized indicators may be worthy of further investigation, and analysts seek to identify and maintain awareness of entities that either fully or partially match the profile attributes over time. We describe INSiGHT (Investigative Search for Graph Trajectories), a comprehensive graph pattern matching technique to find emergent patterns of behaviors in networks and tailor the application to detecting radicalization in the homeland security domain. To account for recurring behavioral indicators and the recency of behaviors as the imminence of a threat, we provide parameterized methods to score multiple occurrences of indicators and to dampen the significance of indicators over time, respectively. Additionally, we provide an indicator categorization scheme and match filtering technique to ensure partial matches to the most salient indicators are identified while reducing the number of false positives. Furthermore, since individuals may be radicalized in small groups or be involved in collective terrorist plots, we introduce a non-combinatorial neighborhood matching technique that enables analysts to use INSiGHT to identify potential query matches from clusters of individuals who may be operating in conspiracies. Our work has resulted in a comprehensive set of tools for social network mining, including natural language processing (NLP) techniques and supervised machine learning models to classify textual data for radicalization behavioral indicators. PINGS (Procedures for Investigative Graph Search) graph database library of procedures for investigative search will be presented, as well as an approach for anonymizing data.

About this Lecture

Number of Slides: 40 - 50
Duration: 50 minutes
Languages Available: English
Last Updated:

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