Online Library

Purpose
With the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.
Design/methodology/approach
This study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.
Findings
Based on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.
Originality/value
At the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.

The Global Internet Forum to Counter Terrorism (GIFCT) commissioned BSR to conduct a human rights assessment of its strategy, governance, and activities. The purpose of this assessment is to identify actual and potential human rights impacts (including both risks and opportunities) arising from GIFCT’s work and make recommendations for how GIFCT and its participants can address these impacts. BSR undertook this human rights review from December 2020 to May 2021. This assessment combines human rights assessment methodology based on the UN Guiding Principles on Business and Human Rights (UNGPs) with consideration of the human rights principles, standards, and methodologies upon which the UNGPs were built. This review was funded by GIFCT, though BSR retained editorial control over its contents.

How does the internet affect the radicalisation of extreme-right lone actor terrorists? In the absence of an established theoretical model, this article identifies six mechanisms seen as particularly relevant for explaining online radicalisation. Having first reviewed a larger set of relevant lone actor terrorists, the study traces these mechanisms in three selected cases where the internet was reportedly used extensively during radicalisation. The findings show that the internet primarily facilitated radicalisation through information provision, as well as amplifying group polarisation and legitimising extreme ideology and violence through echoing. In all three cases, radicalisation was also affected considerably by offline push-factors that through their presence made extreme online messages more impactful. The results challenge the view that offline interaction is necessary for radicalisation to occur but also the view that online influence itself is sufficient.

This paper will examine how extremist organizations manage to present themselves as credible actors in the eyes of potential supporters on social media. This paper will address this question by exploring the role of authenticity in strategic narratives, which is believed to help these groups achieve this purpose. Apart from introducing this new theoretical concept, the paper will also apply it to a new case study of the Russian Imperial Movement (RIM) – the first far-right organization to be designated as terrorist by the U.S. and Canada. By conducting discourse analysis of the group’s social media propaganda on the Russian network VKontakte, the paper will show how the RIM makes its strategic narratives authentic and, as a consequence, creates an image of a credible and trustworthy actor and thus manages to reach out to its target audience.

By reviewing studies of how today’s terrorist and extremist groups operate on social media in conjunction with an overview of U.S. government regulation of terrorist content online, this report finds that stricter U.S. regulation of social media providers may not be the most effective method of combating online terrorist and extremist content.

Proliferation of social media platforms in recent past has resulted into upsurge in the number of users. Advent of these sites have paved way for the users to easily express share and communicate. In such a scenario, it is imperative to analyze the content and identify nasty content so as to avoid unpleasant situations. Machine learning techniques are extensively used for this purpose. In this paper, we propose a language model for the identification of hate speech in twitter data. Distil-BERT, a context aware embedding model along with Support Vector Machine (SVM) for the classification of hate speech has been used. SVM with a 10-fold cross validation and linear kernel has been found to provide better accuracy as compared to existing models. Results show that accuracy is improved with the use of context aware embedding model.