Online Library

Nick Drnaso’s graphic novel Sabrina provides a powerful snapshot of online norms. The picture is not pretty. A young woman goes missing. Her grief-struck boyfriend cannot bear to stay in their home and escapes to a friend’s house. Her sister struggles with the pain of her loss. We learn that the woman’s neighbor, a misogynist loner, killed her and recorded the murder. Online, people clamor for the video. The execution video leaks and goes viral. The media hounds the woman’s sister and her boyfriend. A conspiracy theorist with a popular radio show argues that the murder is a deep-state hoax. He gins up a cyber mob to “investigate” what is really going on. A cyber mob descends. The woman’s family, boyfriend, and her boyfriend’s friend are smeared as crisis actors. They are barraged with death threats, and their personal information is posted far and wide. The attacks continue until a shooting massacre captures the conspiracy theorist’s attention. The cyber mob redirects its wrath at other mourners. The novel raises important questions about the interaction of human behavior, culture, and law in the digital age. What compels people to like, click, and share grotesque execution videos, conspiracy theories, and destructive falsehoods? We have always been drawn to information that resonates with us, but the online environment seems to supercharge human biases. Why? Platforms structure and shape online activity, so what are they doing about online abuse? Each and every one of us is ultimately responsible for liking, clicking, and sharing the destruction. How can we work to change our behavior?

Policy advice for EU member states on how to tackle jihadist online recruitment techniques.

The use of “Big Data” in policy and decision making is a current topic of debate. The 2013 murder of Drummer Lee Rigby in Woolwich, London, UK led to an extensive public reaction on social media, providing the opportunity to study the spread of online hate speech (cyber hate) on Twitter. Human annotated Twitter data was collected in the immediate aftermath of Rigby’s murder to train and test a supervised machine learning text classifier that distinguishes between hateful and/or antagonistic responses with a focus on race, ethnicity, or religion; and more general responses. Classification features were derived from the content of each tweet, including grammatical dependencies between words to recognize “othering” phrases, incitement to respond with antagonistic action, and claims of well-founded or justified discrimination against social groups. The results of the classifier were optimal using a combination of probabilistic, rule-based, and spatial-based classifiers with a voted ensemble meta-classifier. We demonstrate how the results of the classifier can be robustly utilized in a statistical model used to forecast the likely spread of cyber hate in a sample of Twitter data. The applications to policy and decision making are discussed.

The nature of terrorism financing has evolved with the exponential development and expansion of the internet. This dimension of terrorism funding has become a critical source for terrorist organisational mobilisation. The cyber funding of terrorism has coincided with the remarkable technological progress that has been achieved in the last few decades. This chapter provides a general background to the development of cyber terrorism funding. Then, it introduces relevant areas surrounding the cyber dimensions of terrorism financing that are currently challenging scholars, experts, and policymakers alike, including discussions on social media, cryptocurrencies, and the dark web. The chapter then discusses some technological tools and initiatives needed to counter the evolving nature of cyber terrorism funding through artificial intelligence techniques and protocols. Finally, it concludes with a discussion on future challenges. One potential tool that can aid in preventing terrorism funding is the utilisation of artificial intelligence.