Online Library

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.

The development of the Internet and social media has exploded in the last couple of years. Digital environments such as social media and discussion forums provide an effective method of communication and are used by various groups in our societies.  For example, violent extremist groups use social media platforms for recruiting, training, and communicating with their followers, supporters, and donors. Analyzing social media is an important task for law enforcement agencies in order to detect activity and individuals that might pose a threat towards the security of the society.

In this thesis, a set of different technologies that can be used to analyze digital environments from a security perspective are presented. Due to the nature of the problems that are studied, the research is interdisciplinary, and knowledge from terrorism research, psychology, and computer science are required. The research is divided into three different themes. Each theme summarizes the research that has been done in a specific area. The first theme focuses on analyzing digital environments and phenomena. The theme consists of three different studies. The first study is about the possibilities to detect propaganda from the Islamic State on Twitter.  The second study focuses on identifying references to a narrative containing xenophobic and conspiratorial stereotypes in alternative immigration critic media. In the third study, we have defined a set of linguistic features that we view as markers of a radicalization.

Terrorists use cyberspace and social media technology to create fear and spread violent ideologies, which pose a significant threat to public security. Researchers have documented the importance of the application of law and regulation in dealing with the criminal activities in cyberspace. Using routine activity theory, this article assessed the effectiveness of technological approaches to mitigating the expansion and organization of terrorism in cyberspace. Data collection included open-source documents, government threat assessments, legislation, policy papers, and peer-reviewed academic literature and semistructured interviews with fifteen security experts in Nigeria. The key findings were that the new generation of terrorists who are more technological savvy are growing, cybersecurity technologies are effective, and bilateral/multilateral cooperation is essential to combat the expansion of terrorism in cyberspace. The data provided may be useful to stakeholders responsible for national security, counterterrorism, law enforcement on the choice of cybersecurity technologies to confront terrorist expansion in cyberspace.

Since its early mainstream adoption in the 1990s, the Internet has been leveraged by white supremacist groups to recruit and radicalize individuals. Twenty years later, social media platforms, like YouTube, reddit, and Twitter, continue to further this practice. The attention of researchers has been primarily centered on white supremacist men, and this focus on white men erases white women’s roles as active agents in the spread of white supremacy, skewing our understanding of white supremacy as a whole. This study used digital ethnography and interviews to examine the ways white women are radicalized to organized white supremacy through popular social media platforms YouTube, reddit, and Twitter. The study found white women were radicalized by engaging with posts and joining communities focusing on beauty, anti-feminism or “The Red Pill,” traditionalist gender values or #TradLives, and alt-right politics. White supremacist recruiters leveraged gendered topics and weaponized platform features – likes, sharing, comments, recommendation algorithms, etc. – to cultivate a sense of community. Through involvement with these communities, women were introduced to racialized perspectives on each topic, usually after a catalytic pop culture or newsworthy event, and slowly radicalized to organized white supremacy.