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Lone-wolf terrorism is a threat to the security of modern society, as was tragically shown in Norway on July 22, 2011, when Anders Behring Breivik carried out two terrorist attacks that resulted in a total of 77 deaths. Since lone wolves are acting on their own, information about them cannot be collected using traditional police methods such as infiltration or wiretapping. One way to attempt to discover them before it is too late is to search for various ‘‘weak signals’’ on the Internet, such as digital traces left in extremist web forums. With the right tools and techniques, such traces can be collected and analyzed. In this work, we focus on tools and techniques that can be used to detect weak signals in the form of linguistic markers for potential lone wolf terrorism.

This Policy Briefing was commissioned by the Danish government and addresses various methods for tackling online extremism. It draws on discussions which took place at a Policy Planners Network (PPN) meeting held with a range of relevant stakeholders that took place in Copenhagen in June 2013.

Research surrounding radicalization to and use of violence among extremist and terror groups has expanded over the last decade. There are still fundamental questions that must be addressed, particularly regarding the role of the Internet in radicalisation and recruitment as well as general technological skill within extremist groups. Few studies have considered this issue, especially among Far Right groups which have been identified as one of the top threats to public safety within the United States. This exploratory study addresses these issues using a qualitative analysis of a sample of threads from a technology-specific subforum of a widely used web forum in the white nationalist and white power movement. The findings demonstrate that the process of information sharing is distinct from that of more sophisticated deviant and criminal communities on-line, as users readily answer basic technological questions rather than discuss offensive attack techniques. The implications of this study for future research are examined in depth.

Security informatics and intelligence computation plays a vital role in detecting and classifying terrorism contents in the web. Accurate web content classification using the computational intelligence and security informatics will increase the opportunities of the early detection of the potential terrorist activities. In this paper, we propose a modified frequency-based term weighting scheme for accurate Dark Web content classification. The proposed term weighting scheme is compared to the common techniques used in text classification such as Term Frequency (TF), Term Frequency-Inverse Document Frequency (TF-IFD), and Term Frequency- Relative Frequency (tf.rf), on a dataset selected from Dark Web Portal Forum. The experimental results show that the classification accuracy and other evaluation measures based on the proposed scheme outperforms other term weighting techniques based classification.

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The present study applies Social Learning (Differential Association) Theory to the explanation of political violence, focusing on exposure to extremist content through new social media (NSM) and controlling for key variables derived from rival theories. Data are gathered using (a) a paper-and-pencil study among high school students, and (b) a web survey targeting youths between 16 and 24 years old. A total of 6020 respondents form the dataset. Binary logistic regression is used to analyze the data. Results show that even when controlling for background variables, strain variables, personality characteristics, moral values, and peer influences, the statistical association between measures of extremism through NSM (ENSM) and self-reported political violence remains significant and fairly constant. The most persistent effects are found for those measures where individuals actively seek out extremist content on the Internet, as opposed to passive and accidental encounters using NSM. Furthermore, offline differential associations with racist and delinquent peers are also strongly and directly related to self-reported political violence, as are some mechanisms from rival perspectives. This indicates that political violence can only partially be explained by social learning and suggests that the impact of ENSM is mediated by real-world associations and that the offline world has to be taken into account.