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Reacting to a series of terrorist attacks, the European Union has tasked Europol to establish an Internet Referral Unit (EU IRU) to counter terrorist propaganda. The political statement entailed a clear commitment to fundamental rights including data protection. This paper analyses the present and future applicable legal bases and elaborates on challenges connected to the practical implementation of the EU IRU. It also explains why Europol is often referred to as “the most controlled police
agency in the world”. The paper concludes that it is possible to tackle terrorism propaganda online in a
data protection compliant manner

What characterises social media radicals? And why some people become attracted to radicalisation? To explore answers to these questions, a number of tweets posted by a group of suspected radicals tweeting in Arabic were analysed using social network analysis and machine learning. The study revealed that these suspected radicals’ networks showed significant interaction with others; but this interactivity is only significant quantitatively as the interaction is not reciprocated. With regards to why these suspected radicals became attracted to radicalisation, Topic Modelling revealed these suspected radicals’ tweets underpinned a perceived injustice that they believed the Secret Police and the Government inflicted upon them. Overall, the study has shown that data science tools have the potential to inform our understanding of online radicalisation. It is hoped this exploratory study will be the basis for a future study in which the research questions will be answered using a larger sample.

Since the inception of the World Wide Web, security agencies, researchers, and analysts have focused much of their attention on the sentiment found on hate-inspired web-forums. Here, one of their goals has been to detect and measure users’ affects that are expressed in the forums as well as identify how users’ affects change over time. Manual inspection has been one way to do this; however, as the number of discussion posts and sub-forums increase, there has been a growing need for an automated system that can assist humans in their analysis. The aim of this paper, then, is to detect and measure a number of affects expressed in written text on Stormfront.org, the most visited hate forum on the Web. To do this, we used a machine learning approach where we trained a model to recognize affects on three sub-forums: Ideology and Philosophy, For Stormfront Ladies Only, and Stormfront Ireland. The training data consisted of manual annotated data and the affects we focused on were racism, aggression, and worries. Results indicate that even though measuring affects is a subjective process, machine learning is a promising way forward to analyze and measure the presence of different affects on hate forums.

The increased influx of refugees and migrants to the EU in 2015 has been followed by a noticeable presence of online hate speech against migrants in many countries across Europe. The article presents the results of a study of hate speech proliferation on Facebook in the Czech Republic during the summer of 2015. Its goal is to identify the producers of hate speech and determine their social background, explore the main channels of hate speech proliferation, determine the specific groups of migrants targeted by hate speech, put the hate speech in the context of online political communication, and discuss the role of media and politicians in the process of hate speech proliferation. With regard to the works of Castells, Skocpol or Bennett and Segerberg, online hate speech can be perceived as an extreme variety of new, rapidly evolving modes of political communication as such. Social and political activism has been shifting from membership-based organizations and parties towards flexible movements and initiatives with strong emphasis on the logic of identity politics. People may or may not engage in hate speech production as lone independent actors, but they still perceive their actions as part of larger collective efforts. When we focus on hate speech as a form of civic activism or networking, new interesting patterns can emerge.

Academics are increasingly expected to use social media to disseminate their work and knowledge to public audiences. Although this has various advantages, particularly for alternative forms of dissemination, the web can also be an unsafe space for typically oppressed or subordinated groups. This article presents two auto-ethnographic accounts of the abuse and hate academics researching oppressed groups, namely, women and people of Muslim faith, experienced online. In doing so, this article falls into four parts. The first section provides an overview of existing literature, particularly focusing on work which explores the violence and abuse of women and people of Muslim faith online. The second section considers the auto-ethnographic methodological approach adopted in this article. The third section provides the auto-ethnographic accounts of the author’s experiences of hate and abuse online. The final section locates these experiences within broader theoretical concepts, such as silencing, and considers possible implications of such online hate in both an academic context and beyond.

In an effort to better understand the risk of individuals being radicalised via the internet, this paper re-examines the phenomenon of online radicalisation by focusing on four considerations of interest: individual, online environment, interactions between individual and the online environment, and protective elements. A key premise of the discussion presented is that the different theoretical assumptions and linkages underlying each consideration are not only reconcilable but that together they provide a more comprehensive understanding of assessing risk of radicalisation via the internet than any perspective by itself. Implications for operationalising these four considerations and their associated factors to identify individuals at risk of being radicalised via the internet will also be discussed.