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The Arab Spring raised high expectations for political freedom, especially for situations in which the rapid development of ICT intersects with political oppression and rebellion, as was the case in Mali, West Africa. In 2012 the country’s northern part fell into the hands of ‘rebels’ and jihadists were on the rise. This article tries to understand the development of political agency in relation to the unprecedented access to new ICT of the Fulani nomads and urbanites in the Mopti region (Hayre), who engage increasingly with new actors and networks present in the war zone: ‘rebels’ and jihadists; the diaspora from that region; and the journalistic and academic communities who visit the region. We argue that political agency is emerging in the relation between (newly appearing) information networks in both the on- and off-line worlds. These networked societies are embedded in cultural and social historical specificities of the Sudan-Sahel zone in conflict.

Twitter is the largest and most popular micro-blogging website on Internet. Due to low publication barrier, anonymity and wide penetration, Twitter has become an easy target or platform for extremists to disseminate their ideologies and opinions by posting hate and extremism promoting tweets. Millions of tweets are posted on Twitter everyday and it is practically impossible for Twitter moderators or an intelligence and security analyst to manually identify such tweets, users and communities. However, automatic classification of tweets into pre-defined categories is a non-trivial problem problem due to short text of the tweet (the maximum length of a tweet can be 140 characters) and noisy content (incorrect grammar, spelling mistakes, presence of standard and non-standard abbreviations and slang). We frame the problem of hate and extremism promoting tweet detection as a one-class or unary-class categorization problem by learning a statistical model from a training set containing only the objects of one class . We propose several linguistic features such as presence of war, religious, negative emotions and offensive terms to discriminate hate and extremism promoting tweets from other tweets. We employ a single-class SVM and KNN algorithm for one-class classification task. We conduct a case-study on Jihad, perform a characterization study of the tweets and measure the precision and recall of the machine-learning based classifier. Experimental results on large and real-world dataset demonstrate that the proposed approach is effective with F-score of 0.60 and 0.83 for the KNN and SVM classifier respectively.

Dr Prucha discusses how to understand ‘Islamic State’ narratives by analysing their media output. The lecture was organised by VOX-Pol partner institute, International Centre for the Study of Radicalisation (ICSR) and held at King’s College London on 6 July 2015. It focuses on the symbiotic relationships between ideology, media content and strategic action, and on how different messages are targeted at different audiences, was broadcast live on Periscope to wide enthusiasm.

Informal comments to the media by Mr. Jean-Paul Laborde, Assistant Secretary-General and Executive Director of CounterTerrorism Committee Executive Directorate (CTED) and Mrs. Humera Khan, Executive Director of Muflehun, on terrorism online.

ABSTRACT- This paper initially identifies a number of critical distinctions that might help our understanding of the relationship between Internet use and terrorism. It then develops the notion of complex global microstructures as a useful conceptual aid to
understanding how people interact with the Internet in general, and to terrorism in
particular. Parallels are identified between various inappropriate, risky and dangerous
uses of the Internet which are argued to point to a degree of commonality of effect. The
paper concludes by suggesting that some forms of user interaction with the Internet
suggest the Internet may have criminogenic qualities.