Online Library

Studien påvisar bland annat att det finns oändliga möjligheter att använda sig av den digitala arenan i förhållande till förebyggande arbete, samt att integreringen av den digitala arenan är ytterst centralt för att på ett effektivt sätt kunna förebygga och motverka just extremism och våldsbejakande extremism. Detta då en stor del av extremistiska aktörers aktiviteter i dagsläget sker just online, samt då det är tydligt att online- och offlinedimensionen inte kan separeras funktionellt. Det konstateras dock att flera av de befintliga initiativ som studeras främst tenderar att existera som isolerade företeelser, snarare än att exempelvis utgöra en del av en omfattande nationell handlingsplan samt strategi i relation till det förebyggande arbetet mot extremism och våldsbejakande extremism. Trots att det alltså existerar ett flertal intressanta projekt och initiativ med potential att eventuellt kunna integreras och användas i större utsträckning är det i dagsläget både spretigt och fragmenterat – både nationellt och internationellt.

Since the Islamic State proclaimed the Caliphate in 2014, the terrorist organization has been prominent due to the high-quality and efficient distribution of its propaganda, especially in the main online social media platforms. Two of their most popular vehicles for indoctrination and recruitment, the e-magazines Dabiq and Rumiyah, perfectly embody the philosophy of an organization constructed upon a multi-semiotic polarized discourse in which the antagonism between enemies and heroes is stated in many different ways. Using multimodal critical discourse analysis and visual framing as our main theoretical frameworks, this chapter analyses the semiotic structure of the images of foes and allies in the aforementioned magazines to show their essential role within the propaganda machine of the Islamic State, designed to achieve two main interconnected goals: the legitimation of their actions and, through this, the adherence of new fighters to their cause.

This project aims to map right-wing extremism in Victoria through the lens of gender. It begins from the premise that there is an underexplored connection between antifeminist sentiment and far-right extremist sentiment. It does this by focusing on select Victorian-based online groups that have an anti-feminist and far-right profile. The project also works with stakeholders who work in the areas of gender and family violence, to gain insight into their practices and experiences.

Researchers and policymakers have supported a public health approach to countering violent extremism throughout the War on Terror. However, barriers to obtaining primary data include concerns from minority groups about stigmatization, the ethics of harming research subjects by exposing them to violent content, and restrictions on researchers from institutions and governments. Textual analyses of declassified documents from government agencies may overcome these barriers. This article contributes a method for analyzing the predisposing, precipitating, and perpetuating factors of terrorism through open source texts. This method is applied to FBI interrogation summaries of Al Qaeda terrorist Umar Farouk Abdulmutallab who attempted an attack aboard an airplane in 2009. This analysis shows that consuming militant content online led him to narrow his social relationships offline to extremists and foster identifications with subjugated Muslims around the world. After deciding to wage militancy, loyalty to Al Qaeda members, swearing allegiance to and obeying group leaders, and interpreting religious texts militantly perpetuated violent activities. Such work can advance empirical work on militant behavior to develop interventions.

Online video sharing platforms such as YouTube contains several videos and users promoting hate and extremism. Due to low barrier to publication and anonymity, YouTube is misused as a platform by some users and communities to post negative videos disseminating hatred against a particular religion, country or person. We formulate the problem of identification of such malicious videos as a search problem and present a focused-crawler based approach consisting of various components performing several tasks: search strategy or algorithm, node similarity computation metric, learning from exemplary profiles serving as training data, stopping criterion, node classifier and queue manager. We implement two versions of the focused crawler: best-first search and shark search. We conduct a series of experiments by varying the seed, number of n-grams in the language model based comparer, similarity threshold for the classifier and present the results of the experiments using standard Information Retrieval metrics such as precision, recall and F-measure. The accuracy of the proposed solution on the sample dataset is 69% and 74% for the best-first and shark search respectively. We perform characterization study (by manual and visual inspection) of the anti-India hate and extremism promoting videos retrieved by the focused crawler based on terms present in the title of the videos, YouTube category, average length of videos, content focus and target audience. We present the result of applying Social Network Analysis based measures to extract communities and identify core and influential users.

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.