Online Library

To see the author talk about the book, Fighting Terror Online, click on the link ‘The Academic Channel,’ under ‘Related links’ on this webpage.
The unprecedented events that have taken place in recent years have led legislators and governments throughout the world to reconsider and restructure their policies regarding security issues. Today, worldwide attention is being given to a new security threat, in the form of global terrorism. Legal systems are being called upon to provide a response to these threats, in all areas of life, including the online environment.
Among its many tools, global terror also uses advanced technological methods. This fact presents a difficult challenge to policymakers. Therefore, we have chosen to focus this book on the issue of formulating appropriate policy at the interface between security and technology, human rights and economic policy.
The fundamental issue – the tension between security needs and civil rights – is not new. A great deal of experience has been amassed in various countries in this regard, and the question that now arises is whether the existing system of principles and laws, developed on the basis of experience gathered in the “concrete” world, is applicable to the “digital” environment.
This book presents the position that the online environment is a significant and relevant theater of activity in the fight against terror, and will identify the threats, the security needs, and the issues that are unique to this environment. We examine whether the unique characteristics of this environment require new legal solutions, or whether existing solutions are sufficient. Three areas of online activity are identified that require reexamination: security, monitoring, and propaganda. For each of these, we will indicate the issues, examine existing legal arrangements, and offer guidelines for formulating legal policy. There is a demonstrated need to relate to the digital environment as a battlefront, map the new security threats, and thereby hope to provide focus to the pressing discussion on today’s legislative and technological agenda.

VOX-Pol Blog post.

Lessons learned after one year of the NetzDG law.

This Element presents original research into how young people interact with violent extremist material, including terrorist propaganda, when online. It explores a series of emotional and behavioural responses that challenge assumptions that terror or trauma are the primary emotional responses to these online environments. It situates young people’s emotional responses within a social framework, revealing them to have a relatively sophisticated relationship with violent extremism on social media that challenges simplistic concerns about processes of radicalisation. The Element draws on four years of research, including quantitative surveys and qualitative focus groups with young people, and presents a unique perspective drawn from young people’s experiences.

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.

VOX-Pol Blog Post.