Uncovering the internal structure of Boko Haram through its mobility patterns

Boko Haram has caused nearly 40,000 casualties in Nigeria, Niger, Cameroon and Chad, becoming one of the deadliest Jihadist organisations in recent history. At its current rate, Boko Haram takes part in more than two events each day, taking the lives of nearly 11 people daily. Yet, little is known concerning Boko Haram’s internal structure, organisation, and its mobility. Here, we propose a novel technique to uncover the internal structure of Boko Haram based on the sequence of events in which the terrorist group takes part. Data from the Armed Conflict Location & Event Data Project (ACLED) gives the location and time of nearly 3,800 events in which Boko Haram has been involved since the organisation became violent 10 years ago. Using this dataset, we build an algorithm to detect the fragmentation of Boko Haram into multiple cells, assuming that travel costs and reduced familiarity with unknown locations limit the mobility of individual cells. Our results suggest that the terrorist group has a very high level of fragmentation and consists of at least 50–60 separate cells. Our methodology enables us to detect periods of time during which Boko Haram exhibits exceptionally high levels of fragmentation, and identify a number of key routes frequently travelled by separate cells of Boko Haram where military interventions could be concentrated.

[1]  A. Clauset,et al.  On the Frequency of Severe Terrorist Events , 2006, physics/0606007.

[2]  Richard M. Medina,et al.  Social Network Analysis: A case study of the Islamist terrorist network , 2012, Security Journal.

[3]  Carlo Morselli,et al.  Crime and Networks , 2013 .

[4]  Steven M. Radil,et al.  A network approach to the production of geographic context using exponential random graph models , 2019, Int. J. Geogr. Inf. Sci..

[5]  Olivier Walther,et al.  Islamic Terrorism and the Malian Rebellion , 2015 .

[6]  D. Cook The Boko Haram Reader , 2018 .

[7]  H. Milward,et al.  Dark Networks as Problems , 2003 .

[8]  Kathleen M. Carley A Dynamic Network Approach to the Assessment of Terrorist Groups and the Impact of Alternative Courses of Action , 2006 .

[9]  Jacob Zenn Boko Haram’s Factional Feuds: Internal Extremism and External Interventions , 2019, Terrorism and Political Violence.

[10]  Beatriz de la Iglesia,et al.  Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms , 2006, J. Math. Model. Algorithms.

[11]  Weisi Guo,et al.  Common statistical patterns in urban terrorism , 2019, Royal Society Open Science.

[12]  Herbert A. Thelen,et al.  Group Dynamics in Instruction: Principle of Least Group Size , 1949, The School Review.

[13]  Shane D. Johnson,et al.  Space-time analysis of point patterns in Crime and Security events , 2016 .

[14]  Suranjan Weeraratne Theorizing the Expansion of the Boko Haram Insurgency in Nigeria , 2017 .

[15]  Yao-Li Chuang,et al.  Mathematical Models of Radicalization and Terrorism , 2019, ArXiv.

[16]  Sean F. Everton Network Topography, Key Players and Terrorist Networks , 2009 .

[17]  S. Bishop,et al.  Modelling the fear of crime , 2017, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[18]  Zacharias P. Pieri Boko Haram and the Drivers of Islamist Violence , 2019 .

[19]  Marcos A. C. Oliveira,et al.  From Criminal Spheres of Familiarity to Crime Networks , 2015, CompleNet.

[20]  Jeremy R. Backstrom Boko Haram: The History of an African Jihadist Movement , 2019, Democracy and Security.

[21]  Joshua M. Epstein,et al.  Modeling civil violence: An agent-based computational approach , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Zbigniew Smoreda,et al.  Unravelling daily human mobility motifs , 2013, Journal of The Royal Society Interface.

[23]  Kathleen M. Carley,et al.  Pairwise similarity of jihadist groups in target and weapon transitions , 2019, Journal of Computational Social Science.

[24]  H. Savitch Cities in a Time of Terror: Space, Territory, and Local Resilience , 2007 .

[25]  Hilary Matfess Women and the War on Boko Haram: Wives, Weapons, Witnesses , 2017 .

[26]  Herbert H. Tsang,et al.  An agent-based model and computational framework for counter-terrorism and public safety based on swarm intelligence , 2012, Secur. Informatics.

[27]  Kathleen M. Carley,et al.  Modeling and Simulating Terrorist Networks in Social and Geospatial Dimensions , 2007, IEEE Intelligent Systems.

[28]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[29]  Kathleen M. Carley,et al.  A complex networks approach to find latent clusters of terrorist groups , 2019, Appl. Netw. Sci..

[30]  Luke M. Gerdes Illuminating dark networks : the study of clandestine groups and organizations , 2015 .

[31]  Rene M. Bakker,et al.  A preliminary theory of dark network resilience , 2012 .

[32]  Elizabeth Grimm Arsenault,et al.  Disaggregating and Defeating Terrorist Safe Havens , 2015 .

[33]  Malcolm K. Sparrow,et al.  The application of network analysis to criminal intelligence: An assessment of the prospects , 1991 .

[34]  Joshua M Epstein,et al.  Modeling civil violence: An agent-based computational approach , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[35]  Sean F. Everton Disrupting Dark Networks , 2012 .

[36]  Marta C. González,et al.  Sequences of purchases in credit card data reveal lifestyles in urban populations , 2017, Nature Communications.

[37]  Mason A. Porter,et al.  Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction , 2018, SIAM J. Math. Data Sci..

[38]  Elio Marchione,et al.  Event Networks and the Identification of Crime Pattern Motifs , 2015, PloS one.

[39]  David A. Bright,et al.  Disrupting and dismantling dark networks: Lessons from social network analysis and law enforcement simulations , 2015 .

[40]  Daniel E. Agbiboa Ten years of Boko Haram: how transportation drives Africa’s deadliest insurgency , 2020, Cultural Studies.

[41]  Matthew E. Brashears Understanding Dark Networks: A Strategic Framework for the Use of Social Network Analysis , 2017 .

[42]  Benjamin F. Zaitchik,et al.  Lake Chad Total Surface Water Area as Derived from Land Surface Temperature and Radar Remote Sensing Data , 2018, Remote. Sens..

[43]  Christian Leuprecht,et al.  Political Fragmentation and Alliances among Armed Non-state Actors in North and Western Africa (1997–2014) , 2016, ArXiv.

[44]  Carlo Morselli,et al.  The Efficiency/Security Trade-Off in Criminal Networks , 2007, Soc. Networks.

[45]  Christian Leuprecht,et al.  Political Fragmentation and Alliances among Armed Non-state Actors in North and Western Africa (1997–2014) , 2017 .

[46]  Lenka Pitonakova,et al.  Rapid and Near Real-Time Assessments of Population Displacement Using Mobile Phone Data Following Disasters: The 2015 Nepal Earthquake , 2016, PLoS currents.

[47]  Max Gallop,et al.  Networks of Violence: Predicting Conflict in Nigeria , 2020, The Journal of Politics.

[48]  Steven M. Radil,et al.  The Geography of Conflict in North and West Africa , 2020 .

[49]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[50]  Peter Widhalm,et al.  Discovering urban activity patterns in cell phone data , 2015, Transportation.

[51]  Herbert H. Tsang,et al.  An agent-based model and computational framework for counter-terrorism and public safety based on swarm intelligencea , 2012, Security Informatics.

[52]  M. P. D. Montclos Boko Haram and politics : from insurgency to terrorism , 2014 .

[53]  Francesco Marone,et al.  Terrorism and Political Violence , 2014 .

[54]  E. E. Anugwom The Boko Haram Insurgence In Nigeria , 2018 .

[55]  Shane D. Johnson,et al.  Predictive Crime Mapping: Arbitrary Grids or Street Networks? , 2016, Journal of Quantitative Criminology.

[56]  Ryan Miller,et al.  Three is The Answer: Combining Relationships to Analyze Multilayered Terrorist Networks , 2017, 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[57]  Clionadh Raleigh,et al.  Introducing ACLED: An Armed Conflict Location and Event Dataset , 2010 .

[58]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[59]  Aili Malm,et al.  Social Network and Distance Correlates of Criminal Associates Involved in Illicit Drug Production , 2008 .

[60]  George F. Hepner,et al.  Geospatial Analysis of Dynamic Terrorist Networks , 2008 .

[61]  Christian Leuprecht,et al.  The Diffusion and Permeability of Political Violence in North and West Africa , 2016, Terrorism and Political Violence.

[62]  Richard M. Medina,et al.  An Overview of Geographical Perspectives and Approaches in Terrorism Research , 2013 .

[63]  Valdis E. Krebs,et al.  Mapping Networks of Terrorist Cells , 2001 .

[64]  S. Bishop,et al.  Fear of crime: the impact of different distributions of victimisation , 2018, Palgrave Communications.