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A range of psychological, social, and environmental factors render some individuals more susceptible to militant Islam than others. Research also suggests that there are certain “triggers,” which help to explain why it is that only some individuals exposed to the same societal structural influences turn to violence. This article seeks to contribute to future empirical research in this area by studying the significance of certain “charismatic” preachers in this process and examining the role the Internet plays in strengthening the charismatic bond. Difficulties in defining and measuring “charisma” may help in part to explain the paucity of research on this aspect of radicalization but since charismatic authority derives from the bond between preacher and follower, an examination of the activities, strategies, and techniques used to build relationships and win adherents to Salafi-jihadism may provide valuable insights for countering radicalization.

This article offers a descriptive analysis of the propaganda activities of Al Qaeda in the Islamic Maghreb on Internet social media. It examines the group’s propaganda actions from its creation in 1998 until the end of 2015 and argues that the use of social media, Twitter in particular, has failed to offer any real remedy to its mediocre propaganda actions. During the period in which its Twitter profiles were active, the organization continued to manifest the same problems, including a shortage of qualified human resources and poor internal coordination, which had prevented it from engaging in efficient propaganda activity previously. The study of the social media experience of the group offers further evidence of the vulnerabilities of this Maghrebi jihadist organization.

Our country is still in the early stages of the 21st century where technology is advancing on a daily basis allowing the threat of terrorism, both domestic and foreign, to pose a serious risk to both its citizens and its assets if not addressed soon. There are numerous potentially vulnerable sites throughout the country that are still left under guarded and under-protected, specifically my emphasis for this project, the California Independent System Operator (ISO). This multilayered project utilizes public information from the Department of Homeland Security manual. The project also includes information from various national publications of defense principles and security countermeasures, as well as law enforcement protocols in place to deal with these types of security threats and potential breaches, scholarly articles, and industry trade journals.
The California ISO lacks physical and some virtual controls that make it more vulnerable to attacks. Specific recommendations have been made to ensure that the ISO is better protected and can still run an effective business.

On 14 May 2022, an attack was carried out in Buffalo, New York, which resulted in the death of ten individuals and the wounding of three others. The attack was livestreamed online and versions of the footage were disseminated on multiple online services, potentially exposing UK users to content related to terrorism.

As the regulator of UK-established video-sharing platforms (VSPs) and the prospective UK online safety regulator, we sought to learn from the tragic event by reviewing industry responses to the livestreamed attack and cross-industry collaboration to prevent dissemination of associated content.

We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media. We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns. We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content. We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists. All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task. The performance of our framework is extremely promising, yielding in the different forecasting scenarios up to 93% AUC for extremist user detection, up to 80% AUC for content adoption prediction, and finally up to 72% AUC for interaction reciprocity forecasting. We conclude by providing a thorough feature analysis that helps determine which are the emerging signals that provide predictive power in different scenarios.

Until his death in a U.S. drone strike in August 2015, Junaid Hussain was the Islamic State’s most prolific Englishlanguage social media propagandist, working to incite and guide sympathizers in the United Kingdom, United States, and beyond to launch terrorist attacks. Before joining the
jihad in Syria, Hussain was part of a hacking collective in the United Kingdom, focusing much of his attention on perceived injustices against Muslims. In many respects, he was well integrated into British society with his family home in a leafy suburb of Birmingham. A spell in prison contributed to his radicalization and his decision to move to Syria, where he married fellow extremist Sally Jones.