An Ontological Framework to Facilitate Early Detection of 'Radicalization' (OFEDR)-A Three World Perspective

Mental disorder, psychological problems and terrorist behaviour: A systematic review and meta-analysis

Background

The link between mental health difficulties and terrorist behaviour has been the subject of debate for the last 50 years. Studies that report prevalence rates of mental health difficulties in terrorist samples or compare rates for those involved and not involved in terrorism, can inform this debate and the work of those responsible for countering violent extremism.

Objectives

To synthesise the prevalence rates of mental health difficulties in terrorist samples (Objective 1-Prevalence) and prevalence of mental health disorders pre-dating involvement in terrorism (Objective 2-Temporality). The review also synthesises the extent to which mental health difficulties are associated with terrorist involvement compared to non-terrorist samples (Objective 3-Risk Factor).

Search Methods

Searches were conducted between April and June 2022, capturing research until December 2021. We contacted expert networks, hand-searched specialist journals, harvested records from published reviews, and examined references lists for included papers to identify additional studies.

Selection Criteria

Studies needed to empirically examine mental health difficulties and terrorism. To be included under Objective 1 (Prevalence) and Objective 2 (Temporality), studies had to adopt cross-sectional, cohort, or case-control design and report prevalence rates of mental health difficulties in terrorist samples, with studies under Objective 2 also needing to report prevalence of difficulties before detection or involvement in terrorism. For Objective 3 (Risk Factor) studies where there was variability in terrorist behaviour (involved vs. not involved) were included.

Data Collection and Analysis

Captured records were screened in DisillterSR by two authors. Risk of bias was assessed using Joanna Briggs Institute checklists, and random-effects meta-analysis conducted in Comprehensive Meta-Analysis software.

Results

Fifty-six papers reporting on 73 different terrorist samples (i.e., studies) (n = 13,648) were identified. All were eligible for Objective 1. Of the 73 studies, 10 were eligible for Objective 2 (Temporality) and nine were eligible for Objective 3 (Risk Factor). For Objective 1, the life-time prevalence rate of diagnosed mental disorder in terrorist samples (k = 18) was 17.4% [95% confidence interval (CI) = 11.1%-26.3%]. When collapsing all studies reporting psychological problems, disorder, and suspected disorder into one meta-analyses (k = 37), the pooled prevalence rate was 25.5% (95% CI = 20.2%-31.6%). When isolating studies reporting data for any mental health difficulty that emerged before either engagement in terrorism or detection for terrorist offences (Objective 2: Temporality), the life-time prevalence rate was 27.8% (95% CI = 20.9%-35.9%). For Objective 3 (Risk Factor), it was not appropriate to calculate a pooled effect size due the differences in comparison samples. Odds ratios for these studies ranged from 0.68 (95% CI = 0.38-1.22) to 3.13 (95% CI = 1.87-5.23). All studies were assessed as having high-risk of bias which, in part, reflects challenges conducting terrorism research.

Author's Conclusions

This review does not support the assertion that terrorist samples are characterised by higher rates of mental health difficulties than would be expected in the general population. Findings have implications for future research in terms of design and reporting. There are also implications for practice with regards the inclusion of mental health difficulties as indicators of risk.

Method for Detecting Far-Right Extremist Communities on Social Media

Far-right extremist communities actively promote their ideological preferences on social media. This provides researchers with opportunities to study these communities online. However, to explore these opportunities one requires a way to identify the far-right extremists’ communities in an automated way. Having analyzed the subject area of far-right extremist communities, we identified three groups of factors that influence the effectiveness of the research work. These are a group of theoretical, methodological, and instrumental factors. We developed and implemented a unique algorithm of calendar-correlation analysis (CCA) to search for specific online communities. We based CCA on a hybrid calendar correlation approach identifying potential far-right communities by characteristic changes in group activity around key dates of events that are historically crucial to those communities. The developed software module includes several functions designed to automatically search, process, and analyze social media data. In the current paper we present a process diagram showing CCA’s mechanism of operation and its relationship to elements of automated search software. Furthermore, we outline the limiting factors of the developed algorithm. The algorithm was tested on data from the Russian social network VKontakte. Two experimental data sets were formed: 259 far-right communities and the 49 most popular (not far-right) communities. In both cases, we calculated the type II error for two mutually exclusive hypotheses—far-right affiliation and no affiliation. Accordingly, for the first sample, β = 0.81. For the second sample, β = 0.02. The presented CCA algorithm was more effective at identifying far-right communities belonging to the alt-right and Nazi ideologies compared to the neo-pagan or manosphere communities. We expect that the CCA algorithm can be effectively used to identify other movements within far-right extremist communities when an appropriate foundation of expert knowledge is provided to the algorithm.

Modeling the Process of School Shooters Radicalization (Russian Case)

Research on radicalization became relevant to the study of terrorism and violent extremism just two decades ago. The accumulated empirical data on terrorism have led researchers and experts to understand that radicalization is a predictor of violent actions by terrorists, violent extremists, and lone actors. Violent incidents committed by school shooters are not terrorist crimes, but there is good reasons for inclusion as terrorist crimes since they have similar mechanisms. The article aims to create a conceptual model of school-shooter radicalization and determine the distinguishing features of the process. The paper presents a theoretical and methodological base of content analysis concepts, political models, and terrorist radicalization on the different levels of study. Based on the content analysis results, we identify the significant gaps in the research field, consider the radicalization phenomenon in detail, substantiated the qualitative aspects of the school shooters radicalization, and propose a conceptual scheme. Psychological, behavioral, cognitive aspects of the school shooters radicalization reflect a holistic picture of the relationship between the process phases and changes in the parameters of the object’s state. The aspects of radicalization and this phenomenon’s qualitative properties are interpreted as the determinants of the conceptual model. The model includes five stages, each of which is considered to be one of the components for the formation and acceptance of the idea of a violent way to solve a problem, but certainly do not act individually as the only component that leads to the actual implementation of the incident of a school shooting. An in-depth study of online social connections and warning signs, mobilization factors, behavioral trajectories, and imitation mechanisms can help scientists understand why school shooters are increasingly motivated to use violent means to achieve personal goals. We have outlined the possibilities and prospects of the model’s application and directions for future research.