Online Library

As efforts to identify and remove online terrorist and violent extremist content have intensified, concern has also grown about so‐called lawful but awful content. Various options have been touted for reducing the visibility of this borderline content, including removing it from search and recommendation algorithms, downranking it and redirecting those who search for it. This article contributes to this discussion by considering the moderation of such content, in terms of three sets of values. First, definitional clarity. This is necessary to provide users with fair warning of what content is liable to moderation and to place limits on the discretion of content moderators. Yet, at present, definitions of borderline content are vague and imprecise. Second, necessity and proportionality. While downranking and removal from search and recommender algorithms should be distinguished from deplatforming, tech companies’ efforts to deamplify borderline content give rise to many of the same concerns as content removal and account shutdowns. Third, transparency. While a number of platforms now publish their content moderation policies and transparency data reports, these largely focus on violative, not borderline content. Moreover, there remain questions around access to data for independent researchers and transparency at the level of the individual user.

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.

VOX-Pol Blog Post.

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.

VOX-Pol Blog post.

Reciprocal radicalisation is the theory that extremist organisations are connected and feed on one another’s rhetoric and actions to justify violent escalation. Recent empirical work has suggested that reciprocal radicalisation is a good deal more subtle than is often assumed, and is nuanced by organisational, social and political context. This study seeks to apply the theory of reciprocal radicalisation to the far-right digital milieu, an online space conceptualised as underpinning the varying physical manifestations of the far-right. Based on a qualitative thematic analysis of user posts in three far-right web forums, the study concludes that responses to ideologically opposed terrorism within the far-right milieu are often at odds with the assumed radicalising effects of terrorist attacks. While responses were not uniform, for many users in the far-right digital milieu, jihadist terrorism was an obvious and expected result of the wider failures of politics and society. Although there were some calls for violent reprisal, they were juxtaposed by non-violent responses which interpreted jihadist terror as a consequence and sign of societal decadence and political weakness around issues of migration and rights.