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Understanding the psychological risk factors in radicalisation and terrorism is typically limited by both a lack of access to individuals who carry out the acts and those who are willing to engage in research on the matter. The purpose of this study is to describe the process of self-radicalisation of an otherwise law-abiding individual who engaged in single-actor terrorism activities.

A single case study, based on clinical interviews and psychometric testing, of an individual with autism who engaged in multiple acts of terrorism through online activity. The case is presented within existing frameworks of radicalisation, and describes how it developed along the steps described in the path to intended violence.

A number of variables are identified as contributing towards the individual’s vulnerability to radicalisation, such as deficits in higher order cognition, psychopathology, autism spectrum disorder traits, personal interests, social isolation and life stressors.

Unique to this study is how the process of radicalisation and the possibility to carry out the individual’s attacks was made possible only through the use of internet technology.

The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to identify the grievances in Twitter in an Arabian context.

To understand the characteristics of the Twitter users who expressed the identified grievances, data mining techniques and social network analysis were utilised. The study extracted a total of 23,363 tweets and these were stored as a data set. The machine learning algorithm applied to this data set was followed by utilising a data mining process to explore the characteristics of the Twitter feed users. The network of the users was mapped and the individual level of interactivity and network density were calculated.

The machine learning algorithm revealed 12 themes all of which were underpinned by the coalition of Arab countries blockade of Qatar. The data mining analysis revealed that the tweets could be clustered in three clusters, the main cluster included users with a large number of followers and friends but who did not mention other users in their tweets. The social network analysis revealed that whilst a large proportion of users engaged in direct messages with others, the network ties between them were not registered as strong.

Borum (2011) notes that invoking grievances is the first step in the radicalisation process. It is hoped that by understanding these grievances, the study will shed light on what radical groups could invoke to win the sympathy of aggrieved people.

In combination, the machine learning algorithm offered insights into the grievances expressed within the tweets in an Arabian context. The data mining and the social network analyses revealed the characteristics of the Twitter users highlighting identifying and managing early intervention of radicalisation.

The purpose of this study is to compare violent and non-violent extremists in terms of their age when they first perpetrate an extremist act, and to understand how this relates to other factors underlying extremist behaviours. While the end goal of many extremists may be functionally similar, the pathways into extremism vary, and the literature has demonstrated that a “one-size-fits-all” explanation does not exist. Motivational drivers are complex and dynamic; therefore, attempting to identify a terrorist “profile” has limited applied efficacy.

This study applied a temporal approach (“crime script analysis” or CSA) to identify, map and compare the sequential stages (or “scenes”) in the life histories of violent and non-violent extremists who have committed acts of extremism across different age groups. Crime scripts comprising mainly qualitative data for 40 male extremists (20 violent, 20 non-violent “cases”) were developed, and CSA was conducted according to the age at which they committed their first extremist offence.

Results demonstrated key temporal, developmental differences between the pathways of extremists who commit their first offence at different ages. One key difference was that for both the violent and non-violent extremists, those under 30 used the internet as a main means of joining networks and spreading information, whereas the over 30s made more personal, community links.

This research can aid identification of potential environmental triggers and potential increased susceptibility to triggers across certain age groups.

The purpose of this paper is to explore the use of community question answering sites (CQAs) on the topic of terrorism. Three research questions are investigated: what are the dominant themes reflected in terrorism-related questions? How do answer characteristics vary with question themes? How does users’ anonymity relate to question themes and answer characteristics?

Data include 300 questions that attracted 2,194 answers on the community question answering Yahoo! Answers. Content analysis was employed.

The questions reflected the community’s information needs ranging from the life of extremists to counter-terrorism policies. Answers were laden with negative emotions reflecting hate speech and Islamophobia, making claims that were rarely verifiable. Users who posted sensitive content generally remained anonymous.

This paper raises awareness of how CQAs are used to exchange information about sensitive topics such as terrorism. It calls for governments and law enforcement agencies to collaborate with major social media companies to develop a process for cross-platform blacklisting of users and content, as well as identifying those who are vulnerable.

Theoretically, it contributes to the academic discourse on terrorism in CQAs by exploring the type of questions asked, and the sort of answers they attract. Methodologically, the paper serves to enrich the literature around terrorism and social media that has hitherto mostly drawn data from Facebook and Twitter.

The purpose of this paper is to determine the potential predictors of anxiety about digital security, terrorist threats and support for high-tech counter measures.

In Study 1, 195 participants indicated their anxiety about digital security systems, data protection and social networking sites. In Study 2, 107 participants indicated their anxiety about domestic terrorism, international terrorism and extremist groups. In Study 3, 261 participants indicated their support for high-tech counter-terrorism measures.

Study 1 suggests that whereas anxiety about digital security systems, data protection and social networking sites was positively predicted by right-wing authoritarianism, anxiety about social networking was also negatively predicted by time spent online. Study 2 shows that time spent online was a negative predictor of anxiety about domestic terrorism. Study 3 indicates that the strongest positive predictor of support for all the measures was right-wing authoritarianism, followed by national identity.

The findings show the relevance of terror management theory to digital security and counter-terrorism.

It appears that right-wing authoritarianism and national identity may serve as mechanisms for people to subjectively counter the presented threats. This notion may inform relevant policy and practice aimed at making communities safer and potentially helps introduce counter-terror measures with less public backlash.

When designing counter-terror measures, policy makers should consider compound national identities (e.g. Catalan or Basque people).

The paper makes contribution to under-explored areas of terrorism anxiety and support for counter-terror measures.

With the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.

This study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.

Based on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.

At the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.

This paper aims to examine the trends in research studies in the past decade which address the use and analysis of propaganda in social media using natural language processing. The purpose of this study is to conduct a comprehensive bibliometric review of studies focusing on the use, identification and analysis of propaganda in social media.

This work investigates and examines the research papers acquired from the Scopus database which has huge number of peer reviewed literature and also provides interfaces to access required for bibliometric study. This paper has covered subject papers from 2010 to early 2020 and using tools such as VOSviewer and Biblioshiny.

This bibliometric survey shows that propaganda in social media is more studied in the area of social sciences, and the field of computer science is catching up. The evolution of research for propaganda in social media shows positive trends. This subject is primarily rooted in the social sciences. Also this subject has shown a recent shift in the area of computer science. The keyword analysis shows that the propaganda in social media is being studied in conjunction with issues such as fake news, political astroturfing, terrorism and radicalization.

The lack of highly cited papers and co-citation analysis implies intermittent contributions by the researchers. Propaganda in social media is becoming a global phenomenon, and ill effects of this are evident in developing countries as well. This denotes a great deal of scope of work for researchers in other countries focusing on their territorial issues. This study was conducted in the confines of data captured from the Scopus database. Hence, it should be noted that some vital publications in recent times could not be included in this study.

The uniqueness of this work is that a thorough bibliometric analysis of the topic is demonstrated using several forms such as mind map, co-occurrence, co-citations, Sankey plot and topic dendrograms by using bibliometric tools such as VOSviewer and Biblioshiny.

The global prevalence of social media and its potential to cause polarization are highly debated and impactful. The previous literature often assumes that the ideological bias of any media outlet remains static and exogenous to the polarization process. By studying polarization as a whole from an ecosystem approach, the authors aim to identify policies and strategies that can help mitigate the adverse effects of polarization and promote healthier online discourse.

To investigate online polarization, the authors perform a systematic review and analysis of approximately 400 research articles to explore the connection between cognitive bias and polarization, examining both causal and correlational evidence. The authors extensively evaluate and integrate existing research related to the correlation between online polarization and crucial factors such as public engagement, selective exposure and political democracy. From doing so, the authors then develop a PolarSphere ecosystem that captures and illustrates the process of online polarization formation.

The authors' review uncovers a wide range of associations, including ideological cognition, bias, public participation, misinformation and miscommunication, political democracy, echo chambers and selective exposure, heterogeneity and trust. Although the impact of bias on social media polarization depends on specific environments and internal/external conditions, certain variables exhibit strong associations across multiple contexts. The authors use these observations as a basis from which to construct PolarSphere, an ecosystem of bias-based polarization on social media, to theorize the process of polarization formation.

Based on the PolarSphere ecosystem, the authors argue that it is crucial for governments and civil societies to maintain vigilance and invest in further research to gain a deep comprehension of how cognitive bias affects online polarization, which could lead to ways to eliminate polarization.

Social media use in autistic people has been identified as a potential avenue for less pressured social contact. Given shifts towards online socialisation, this study aims to systematically review the available literature on how autistic people experience and use social media.

A systematic review was carried out using the preferred reporting items for systematic reviews and meta-analyses system, with relevant literature reviewed and synthesised using a narrative approach. Inclusion criteria included adults and/or adolescents as the sole sample, a focus on autism and social media use as the sole/main topic.

A total of 1,278 research studies were initially identified; following screening, 15 papers were reviewed. Studies demonstrated some support for the positive role social media can play and presented its use as a smart adaptation for the challenges of neurotypical socialising. For autistic people, social media may provide a balance between meeting the need to be connected to people without the pressure of managing aspects, such as tone of voice or body language.

Most studies were questionnaire-based with substantial threats to validity. Samples were self-selecting, with autism status not always confirmed. Individual studies used a broad approach to the definition of social media, including online gaming and other online activities, precluding firm conclusions being made.

There may be a Goldilocks “just right” level of social media contact that could maximise connectedness to others without impacting negatively on mental health, offline relationships and activities of daily living.

This paper presents a novel account of research from case studies and larger scale questionnaire studies.

Global evidence suggests a potential displacement of youth offending from the physical to the digital landscape, requiring revision of existing detection and intervention methods. This study aims to explore pathways from harmful to illegal online activity perpetrated by young people, legislation and police perspectives, current detection methods and interventions.

This perspective paper examines issues observed within a larger systematic literature review on digital youth offending.

A trajectory from acceptable to harmful and subsequently illegal behaviour was identified, with a particular pathway from unethical video game activity to digitally dependent offending. Legislation and police perspectives vary by jurisdiction, with a common theme that increased officer education is key to the level of preparedness to investigate cases. Machine learning and automatic prevention show promise as detection and disruption processes, with education recommended for young people as a deterrent and redirection of skills to positive outcomes.

Recommendations for further research include a broad survey of school students to include all identified areas of digital offending, which could drive the development of targeted education by law enforcement and partner agencies for young people.

The shift in youth offending requires the justice and educational systems to adjust how they respond to youth crime. Policy and practise shifts can include further exploration of investigative hacking, education for law enforcement and educational prevention and redirection programmes aimed at youth.

The digital displacement of youth offending is a progressively emerging concept. This paper examines the current state of response from educational and law enforcement agencies and discusses the next steps based on what is currently known.