The SOMA Terror Organization Portal (STOP): social network and analytic tools for the real-time analysis of terror groups

Sliva, Amy; Subrahmanian, V. S.; Martinez, Vanina; Simari, Gerardo I.

doi:10.1007/978-0-387-77672-9_2

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…These systems have been used in prior case studies of real-world security situations, such as the behavior of Hezbollah [16] and Hamas [17]. These data analytics and access to the CMOT dataset are available in the SOMA Terror Organization Portal (STOP) [18], a system designed to make models, forecasts, and analyses of terror group behavior accessible to intelligence analysts and experts.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

A Computationally-Enabled Analysis of Lashkar-e-Taiba Attacks in Jammu and Kashmir

Mannes

Shakarian

Sliva

et al. 2011

2011 European Intelligence and Security Informatics Conference

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Section: Related Work and Conclusionmentioning

confidence: 99%

A Computationally-Enabled Analysis of Lashkar-e-Taiba Attacks in Jammu and Kashmir

Mannes

Shakarian

Sliva

et al. 2011

2011 European Intelligence and Security Informatics Conference

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“…The Stochastic Opponent Modeling Agent (SOMA) was originally developed to understand how social, economics or religious communities behave; later it was specifically applied to terrorist organizations [227,228,229]. Datasets tracking rebellion and protest movements worldwide are mined to generate rules of behavior for known violent ethno-political or ethno-nationalist groups [230,231].…”

Section: Lattice and Network Modelsmentioning

confidence: 99%

Mathematical Models of Radicalization and Terrorism

Chuang,

D'Orsogna

2019

Preprint

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The rapid spread of radical ideologies has led to a world-wide succession of terrorist attacks in recent years. Understanding how extremist tendencies germinate, develop, and drive individuals to action is important from a cultural standpoint, but also to help formulate response and prevention strategies. Demographic studies, interviews with radicalized subjects, analysis of terrorist databases, reveal that the path to radicalization occurs along progressive steps, where age, social context and peer-to-peer exchanges play major roles. To execute terrorist attacks, radicals must efficiently communicate with one another while maintaining secrecy; they are also subject to pressure from counter-terrorism agencies, public opinion and the need for material resources. Similarly, government entities must gauge which intervention methods are most effective. While a complete understanding of the processes that lead to extremism and violence, and of which deterrents are optimal, is still lacking, mathematical modelers have contributed to the discourse by using tools from statistical mechanics and applied mathematics to describe existing and novel paradigms, and to propose novel counter-terrorism strategies. We review some of their approaches in this work, including compartment models for populations of increasingly extreme views, continuous time models for age-structured radical populations, radicalization as social contagion processes on lattices and social networks, agent based models, game theoretic formulations. We highlight the useful insights offered by analyzing radicalization and terrorism through quantitative frameworks. Finally, we discuss the role of institutional intervention and the stages at which deradicalization strategies might be most effective.

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“…Its advantage over Schrodt's approach and Bond and his colleagues' approach is that building a behavioral model is fully automated and fast. 7 Using the behavioral rules and a linear-programming-based algorithm, SOMA generates the prediction. No detailed analysis of SOMA's accuracy is available yet (the SOMA Forecast Engine is still under development).…”

mentioning

confidence: 99%

“…[3][4][5][6][7] SOMA looks at the data collected about a given group and automatically extracts stochastic behavioral rules. Its advantage over Schrodt's approach and Bond and his colleagues' approach is that building a behavioral model is fully automated and fast.…”

mentioning

confidence: 99%