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Social networks are being used by terrorist organizations to distribute messages with the intention of influencing people and recruiting new members. The research presented in this paper focuses on the analysis of Twitter messages to detect the leaders orchestrating terrorist networks and their followers. A big data architecture is proposed to analyze messages in real time in order to classify users according to diferent parameters like level of activity, the ability to infuence other users, and the contents of their messages. Graphs have been used to analyze how the messages propagate through the network, and this involves a study of the followers based on retweets and general impact on other users. Ten, fuzzy clustering techniques were used to classify users in profiles, with the advantage over other classifcations techniques of providing a probability for each profile instead of a binary categorization. Algorithms were tested using public database from Kaggle and other Twitter extraction techniques. The resulting profiles detected automatically by the system were manually analyzed, and the parameters that describe each profile correspond to the type of information that any expert may expect. Future applications are not limited to detecting terrorist activism. Human resources departments can apply the power of profle identification to automatically classify candidates, security teams can detect undesirable clients in the financial or insurance sectors, and immigration officers can extract additional insights with these
techniques.

As liberal democracies grapple with the evolution of online political extremism, in addition to governments, social media and internet infrastructure companies have found themselves making more and more decisions about who gets to use their platforms, and what people say online. This raises the question that this paper explores, who should regulate extremist content online? In doing so the first part of the paper examines the evolution of the increasing role that social media and internet infrastructure companies have come to play in the regulating extremist content online, and the ethical challenges this presents. The second part of the paper explores three ethical challenges: i) the moral legitimacy of private actors, ii) the concentration of power in the hands of a few actors and iii) the lack of separation of powers in the content regulation process by private actors.

This study, commissioned by the European Parliament’s Policy Department for
Citizens’ Rights and Constitutional Affairs at the request of the LIBE Committee,
provides an overview of current approaches to countering terrorist narratives. The
first and second sections outline the different responses developed at the global
and European Union levels. The third section presents an analysis of four different
approaches to responding to terrorist narratives: disruption of propaganda
distribution, redirect method, campaign and message design, and government
communications and synchronisation of message and action. The final section
offers a number of policy recommendations, highlighting five interrelated ‘lines of
effort’ essential to maximising the efficiency and effectiveness of counterterrorism
and countering violent extremism strategic communication.

VOX-Pol Blog post.

VOX-Pol Blog post.

Over recent years, there have been rapid advances in information and communication technology. The Internet of Things (IoT), an instance of such technologies, has brought numerous benefits to societies, revolutionising the lifestyles of many individuals living in these societies. Whilst advances in the IoT undoubtedly offer numerous benefits, they simultaneously present a wide range of new security threats that can have devastating impacts on societies.

This chapter aims to examine security risks posed by or against the IoT ecosystem from a counterterrorism perspective. To this end, the chapter will briefly examine the potential implications of the IoT phenomenon this for terrorism and hence counterterrorism.