Online Library

Using a Vector-Autoregressive (VAR) model, this paper analyzes the relationship between Islamist terrorist attacks and Internet searches for the phrases such as “join Jihad” or “join ISIS.” It was found that Internet searches for “join Jihad” and “taghut” (Arabic word meaning “to rebel”) preceded the Islamist terrorist attacks by three weeks over the period January 2014 to December 2016. Internet searches for “kufar” (the derogatory Arabic word for non-Muslims) preceded the attacks that resulted in deaths from the Islamist terrorist groups. Casualties, including those injured and killed by the Islamist groups, were also found to precede Internet searches for “join Jihad” and “ISIS websites.” Countermeasures to the usage of social media for terrorist activity are also discussed. As an example, if Internet searches for specific terms can be identified that precede a terrorist attack, authorities can be on alert to possibly stop an impending attack. Chat rooms and online discussion groups can also be used to disseminate information to argue against terrorist propaganda that is being released.

Kølvraa’s article focuses on the cultural imaginary of the Scandinavian extreme right by analysing the online presence of the so-called Nordic Resistance Movement. He seeks to show how the cultural imaginaries of this National Socialist organization make use of the Scandinavian Viking heritage in three distinct ways. First, to produce a distinctly Nordic form of National Socialism and thus potentially make this ideology palatable to Nordic publics. Second, to differentiate their racially oriented political project from a wider far-right or populist right concern with the defence of European Christian heritage and/or civilization against Islam. And, third, to thematize and perform a certain hyper-masculine identity, especially in the context of martial and sporting competitions arranged by the organization.

This paper presents a review of intervention programmes against cyberhate. Over the last decade, the preoccupation over the use of electronic means of communication as a tool to convey hate, racist and xenophobic contents rose tremendously. NGOs, legal professionals, private companies, and civil society have developed interventions but little is known about their impact. For this review we followed the method and protocol from the guidelines from the Cochrane Collaboration Handbook for Systematic Reviews and the Campbell Collaboration Crime and Justice guidelines. The review identified three key intervention areas: law, technology and education through the empowerment of the individuals under the form of counter-speech. No specific intervention towards aggressors was found and most projects focus on prevention or victims through confidence building and skills learning to speak out, report and potentially react in an appropriate way. We did not find any rigorously assessed interventions, which highlights a gap in research and stresses the need for this type of studies. The evaluation of effectiveness of interventions needs to be included in the near future research agenda. Up to now, although intentions are good, we have no evidence that the steps that are undertaken are effective in preventing and reducing cyberhate.

By facilitating the flow of information in society, communications technology (CT; e.g., newspapers, radio, television, the Internet) can help terrorists to (i) spread their message, (ii) recruit followers, and (iii) coordinate among group members. However, CT also facilitates monitoring and arresting terrorists. This article formulates the hypothesis that a society’s level of CT is systematically related to terrorism. We introduce a simple theoretical framework, suggesting that terrorism first becomes more attractive with a rise in CT, but then decreases, following an inverted U shape. Accessing data for 199 countries from 1970 to 2014, we find evidence consistent with these predictions: terrorism peaks at intermediate ranges of CT and corresponding magnitudes are sizable. Our estimations control for a range of potentially confounding factors, as well as country fixed effects and year fixed effects. Results are robust to a battery of alternative specifications and placebo regressions. We find no evidence of a potential reporting bias explaining our findings.

This special responsive issue on “Platform Surveillance” critically assesses the surveillance dimensions and politics of large-scale digital platforms. The issue includes an editorial introduction to the topic and its implications, dozens of articles on specific platforms or platform trends, three book reviews of Shoshana Zuboff’s The Age of Surveillance Capitalism, and an interview with Zuboff about her work.

In recent decades, information technology went through an explosive evolution, revolutionizing the way communication takes place, on the one hand enabling the rapid, easy and almost costless digital interaction, but, on the other, easing the adoption of more aggressive communication styles. It is crucial to regulate and attenuate these behaviors, especially in the digital context, where these emerge at a fast and uncontrollable pace and often cause severe damage to the targets. Social networks and other entities tend to channel their efforts into minimizing hate speech, but the way each one handles the issue varies. Thus, in this thesis, we investigate the problem of hate speech detection in social networks, focusing directly on Twitter. Our first goal was to conduct a systematic literature review of the topic, targeting mostly theoretical and practical approaches. We exhaustively collected and critically summarized mostly recent literature addressing the topic, highlighting popular definitions of hate, common targets and different manifestations of such behaviors. Most perspectives tackle the problem by adopting machine learning approaches, focusing mostly on text mining and natural language processing techniques, on Twitter. Other authors present novel features addressing the users themselves. Although most recent approaches target Twitter, we noticed there were few tools available that would address this social network platform or tweets in particular, considering their informal and specific syntax. Thus, our second goal was to develop a tokenizer able to split tweets into their corresponding tokens, taking into account all their particularities. We performed two binary hate identification experiments, having achieved the best f-score in one of them using our tokenizer. We used our tool in the experiments conducted in the following chapters. As our third goal, we proposed to assess which text-based features and preprocessing techniques would produce the best results in hate speech detection. During our literature review, we collected the most common preprocessing, sentiment and vectorization features and extracted the ones we found suitable for Twitter in particular. We concluded that preprocessing the data is crucial to reduce its dimensionality, which is often a problem in small datasets. Additionally, the f-score also improved. Furthermore, analyzing the tweets’ semantics and extracting their character n-grams were the tested features that better improved the detection of hate, enhancing the f-score by 1.5% and the hate recall by almost 5% on unseen testing data. On the other hand, analyzing the tweets’ sentiment didn’t prove to be helpful. Our final goal derived from a lack of user-based features in the literature. Thus, we investigated a set of features based on profiling Twitter users, focusing on several aspects, such as the gender of authors and mentioned users, their tendency towards hateful behaviors and other characteristics related to their accounts (e.g. number of friends and followers). For each user, we also generated an ego network, and computed graph-related statistics (e.g. centrality, homophily), achieving significant improvements – f-score and hate recall increased by 5.7% and 7%, respectively.