Online Library

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.

This article offers a descriptive analysis of the private interactions which took place on the jihadist Internet forum known as Ansar Al Mujahideen between 2008 and 2010. The analysis of the non-visible part of the forum contributes to a more robust underpinning of some current assumptions regarding the jihadist Internet infrastructure and its hierarchical dependence on, and subordination to, formal terrorist organisations and charismatic leaders. In addition, it offers a new perspective on other aspects such as the many conflicts and rivalries between the different forums, the operational constraints caused by the lack of human and material resources, and the considerable vulnerability of the forums to cyber-sabotage and infiltration attempts.

The Woolwich attack in May 2013 has led to a spate of hate crimes committed against Muslim communities in the United Kingdom. These incidents include Muslim women being targeted for wearing the headscarf and mosques being vandalized. While street level Islamophobia remains an important area of investigation, an equally disturbing picture is emerging with the rise in online anti-Muslim abuse. This article argues that online Islamophobia must be given the same level of attention as street level Islamophobia. It examines 500 tweets from 100 different Twitter users to examine how Muslims are being viewed and targeted by perpetrators of online abuse via the Twitter search engine, and offers a typology of offender characteristics.

VOX-Pol Summer School: ‘Topics in Violence Online Political Extremism’
Dublin: July 3, 2014