ON META-NETWORKS, DEEP LEARNING, TIME AND JIHADISM

Il terrorismo di stampo jihadista rappresenta una minaccia per la società e una sfida per gli scienziati interessati a comprenderne la complessità. Questa complessità richiede costantemente nuovi sviluppi in termini di ricerca sul terrorismo. Migliorare la conoscenza empirica rispetto a tale fenomeno può potenzialmente contribuire a sviluppare applicazioni concrete e, in ultima istanza, a prevenire danni all’uomo. In considerazione di tali aspetti, questa tesi presenta un nuovo quadro metodologico che integra scienza delle reti, modelli stocastici e apprendimento profondo per far luce sul terrorismo jihadista sia a livello esplicativo che predittivo. In particolare, questo lavoro compara e analizza le organizzazioni jihadiste più attive a livello mondiale (ovvero lo Stato Islamico, i Talebani, Al Qaeda, Boko Haram e Al Shabaab) per studiarne i pattern comportamentali e predirne le future azioni. Attraverso un impianto teorico che si poggia sulla concentrazione spaziale del crimine e sulle prospettive strategiche del comportamento terroristico, questa tesi persegue tre obiettivi collegati utilizzando altrettante tecniche ibride. In primo luogo, verrà esplorata la complessità operativa delle organizzazioni jihadiste attraverso l’analisi di matrici stocastiche di transizione e verrà presentato un nuovo coefficiente, denominato “Normalized Transition Similarity”, che misura la somiglianza fra paia di gruppi in termini di dinamiche operative. In secondo luogo, i processi stocastici di Hawkes aiuteranno a testare la presenza di meccanismi di dipendenza temporale all’interno delle più comuni sotto-sequenze strategiche di ciascun gruppo. Infine, il framework integrerà la meta-reti complesse e l’apprendimento profondo per classificare e prevedere i target a maggiore rischio di essere colpiti dalle organizzazioni jihadiste durante i loro futuri attacchi. Per quanto riguarda i risultati, le matrici stocastiche di transizione mostrano che i gruppi terroristici possiedono un ricco e complesso repertorio di combinazioni in termini di armi e obiettivi. Inoltre, i processi di Hawkes indicano la presenza di diffusa self-excitability nelle sequenze di eventi. Infine, i modelli predittivi che sfruttano la flessibilità delle serie temporali derivanti da grafi dinamici e le reti neurali Long Short-Term Memory forniscono risultati promettenti rispetto ai target più a rischio. Nel complesso, questo lavoro ambisce a dimostrare come connessioni astratte e nascoste fra eventi possano essere fondamentali nel rivelare le meccaniche del comportamento jihadista e come processi memory-like (ovvero molteplici comportamenti ricorrenti, interconnessi e non randomici) possano risultare estremamente utili nel comprendere le modalità attraverso cui tali organizzazioni operano.Jihadist terrorism represents a global threat for societies and a challenge for scientists interested in understanding its complexity. This complexity continuously calls for developments in terrorism research. Enhancing the empirical knowledge on the phenomenon can potentially contribute to developing concrete real-world applications and, ultimately, to the prevention of societal damages. In light of these aspects, this work presents a novel methodological framework that integrates network science, mathematical modeling, and deep learning to shed light on jihadism, both at the explanatory and predictive levels. Specifically, this dissertation will compare and analyze the world's most active jihadist terrorist organizations (i.e. The Islamic State, the Taliban, Al Qaeda, Boko Haram, and Al Shabaab) to investigate their behavioral patterns and forecast their future actions. Building upon a theoretical framework that relies on the spatial concentration of terrorist violence and the strategic perspective of terrorist behavior, this dissertation will pursue three linked tasks, employing as many hybrid techniques. Firstly, explore the operational complexity of jihadist organizations using stochastic transition matrices and present Normalized Transition Similarity, a novel coefficient of pairwise similarity in terms of strategic behavior. Secondly, investigate the presence of time-dependent dynamics in attack sequences using Hawkes point processes. Thirdly, integrate complex meta-networks and deep learning to rank and forecast most probable future targets attacked by the jihadist groups. Concerning the results, stochastic transition matrices show that terrorist groups possess a complex repertoire of combinations in the use of weapons and targets. Furthermore, Hawkes models indicate the diffused presence of self-excitability in attack sequences. Finally, forecasting models that exploit the flexibility of graph-derived time series and Long Short-Term Memory networks provide promising results in terms of correct predictions of most likely terrorist targets. Overall, this research seeks to reveal how hidden abstract connections between events can be exploited to unveil jihadist mechanics and how memory-like processes (i.e. multiple non-random parallel and interconnected recurrent behaviors) might illuminate the way in which these groups act

A Policy-oriented Agent-based Model of Recruitment into Organized Crime

Criminal organizations exploit their presence on territories and local communities to recruit new workforce in order to carry out their criminal activities and business. The ability to attract individuals is crucial for maintaining power and control over the territories in which these groups are settled. This study proposes the formalization, development and analysis of an agent-based model (ABM) that simulates a neighborhood of Palermo (Sicily) with the aim to understand the pathways that lead individuals to recruitment into organized crime groups (OCGs). Using empirical data on social, economic and criminal conditions of the area under analysis, we use a multi-layer network approach to simulate this scenario. As the final goal, we test different policies to counter recruitment into OCGs. These scenarios are based on two different dimensions of prevention and intervention: (i) primary and secondary socialization and (ii) law enforcement targeting strategies.Comment: 15 pages, 2 figures. Paper accepted and in press for the Proceedings of the 2019 Social Simulation Conference (Mainz, Germany

Recruitment into organised criminal groups: A systematic review

This paper provides a narrative synthesis of the results of a systematic review of the social, psychological and economic factors leading to recruitment into organised crime. This is based on the analysis of evidence emerging from 47 qualitative, quantitative and mixed-method studies published in or before 2017. While the selected studies varied markedly in method and quality, several factors emerged as particularly important in understanding recruitment into organised criminal groups. These included the role of social relations (family, kinship, friendship and work-relations), criminal background and criminal skills

Life-Course Criminal Trajectories of Mafia Members

Through a novel data set comprising the criminal records of 11,138 convicted mafia offenders, we compute criminal career parameters and trajectories through group-based trajectory modeling. Mafia offenders report prolific and persistent careers (16.1 crimes over 16.5 years on average), with five distinct trajectories (low frequency, high frequency, early starter, moderate persistence, high persistence). While showing some similarities with general offenders, the trajectories of mafia offenders also exhibit significant differences, with several groups offending well into their middle and late adulthood, notwithstanding intense criminal justice sanctions. These patterns suggest that several mafia offenders are life-course persisters and career criminals and that the involvement in the mafias is a negative turning point extending the criminal careers beyond those observed in general offenders

Organized crime groups: A systematic review of individual‐level risk factors related to recruitment

Background Studies from multiple contexts conceptualize organized crime as comprising different types of criminal organizations and activities. Notwithstanding growing scientific interest and increasing number of policies aiming at preventing and punishing organized crime, little is known about the specific processes that lead to recruitment into organized crime. Objectives This systematic review aimed at (1) summarizing the empirical evidence from quantitative, mixed methods, and qualitative studies on the individual-level risk factors associated with the recruitment into organized crime, (2) assessing the relative strength of the risk factors from quantitative studies across different factor categories and subcategories and types of organized crime. Methods We searched published and unpublished literature across 12 databases with no constraints as to date or geographic scope. The last search was conducted between September and October 2019. Eligible studies had to be written in English, Spanish, Italian, French, and German. Selection Criteria Studies were eligible for the review if they: Reported on organized criminal groups as defined in this review. Investigated recruitment into organized crime as one of its main objectives. Provided quantitative, qualitative, or mixed methods empirical analyses. Discussed sufficiently well-defined factors leading to recruitment into organized crime. Addressed factors at individual level. For quantitative or mixed-method studies, the study design allowed to capture variability between organized crime members and non-members. Data Collection and Analysis From 51,564 initial records, 86 documents were retained. Reference searches and experts' contributions added 116 additional documents, totaling 202 studies submitted to full-text screening. Fifty-two quantitative, qualitative, or mixed methods studies met all eligibility criteria. We conducted a risk-of-bias assessment of the quantitative studies while we assessed the quality of mixed methods and qualitative studies through a 5-item checklist adapted from the CASP Qualitative Checklist. We did not exclude studies due to quality issues. Nineteen quantitative studies allowed the extraction of 346 effect sizes, classified into predictors and correlates. The data synthesis relied on multiple random effects meta-analyses with inverse variance weighting. The findings from mixed methods and qualitative studied were used to inform, contextualize, and expand the analysis of quantitative studies. Results The amount and the quality of available evidence were weak, and most studies had a high risk-of-bias. Most independent measures were correlates, with possible issues in establishing a causal relation with organized crime membership. We classified the results into categories and subcategories. Despite the small number of predictors, we found relatively strong evidence that being male, prior criminal activity, and prior violence are associated with higher odds of future organized crime recruitment. There was weak evidence, although supported by qualitative studies, prior narrative reviews, and findings from correlates, that prior sanctions, social relations with organized crime involved subjects, and a troubled family environment are associated with greater odds of recruitment. Authors' Conclusions The available evidence is generally weak, and the main limitations were the number of predictors, the number of studies within each factor category, and the heterogeneity in the definition of organized crime group. The findings identify few risk factors that may be subject to possible preventive interventions

Homicides involving Black victims are less likely to be cleared in the United States

<p>This repository contains information, data, and software to replicate the analyses contained in the paper "<strong>Homicides involving Black victims are less likely to be cleared in the United States</strong>" forthcoming at <i>Criminology</i>. Find information on how to reproduce the results in the readme.txt file.</p&gt

Pairwise similarity of jihadist groups in target and weapon transitions

Tactical decisions made by jihadist groups can have extremely negative impacts on societies. Studying the characteristics of their attacks over time is therefore crucial to extract relevant knowledge on their operational choices. In light of this, the present study employs transition networks to construct trails and analyze the behavioral patterns of the world\u2019s five most active jihadist groups using open access data on terror attacks from 2001 to 2016. Within this frame, we propose Normalized Transition Similarity (NTS), a coefficient that captures groups\u2019 pairwise similarity in terms of transitions between different temporally ordered sequences of states. For each group, these states respectively map attacked targets, employed weapons, and targets and weapons combined together with respect to the entire sequence of attacks. Analyses show a degree of stability of results among a number of pairs of groups across all trails. With this regard, Al Qaeda and Al Shabaab exhibit the highest NTS scores, while the Taliban and Al Qaeda prove to be the most different groups overall. Finally, potential policy implications and future work directions are also discussed