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VOX-Pol Blog post.

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.

The advent of the Internet inadvertently augmented the functioning and success of violent extremist organizations. Terrorist organizations like the Islamic State in Iraq and Syria (ISIS) use the Internet to project their message to a global audience. The majority of research and practice on web‐based terrorist propaganda uses human coders to classify content, raising serious concerns such as burnout, mental stress, and reliability of the coded data. More recently, technology platforms and researchers have started to examine the online content using automated classification procedures. However, there are questions about the robustness of automated procedures, given insufficient research comparing and contextualizing the difference between human and machine coding. This article compares output of three text analytics packages with that of human coders on a sample of one hundred nonindexed web pages associated with ISIS. We find that prevalent topics (e.g., holy war) are accurately detected by the three packages whereas nuanced concepts (Lone Wolf attacks) are generally missed. Our findings suggest that naïve approaches of standard applications do not approximate human understanding, and therefore consumption, of radicalizing content. Before radicalizing content can be automatically detected, we need a closer approximation to human understanding.

This Article analyzes intermediaries’ civil liability for terror attacks under the anti-terror statutes and other doctrines in tort law. It aims to contribute to the literature in several ways. First, it outlines the way intermediaries aid terrorist activities either willingly or unwittingly. By identifying the role online intermediaries play in terrorist activities, one may lay down the first step towards creating a legal policy that would mitigate the harm caused by terrorists’ incitement over the internet. Second, this Article outlines a minimum standard of civil liability that should be imposed on intermediaries for speech made by terrorists on their platforms. Third, it highlights the contradictions between intermediaries’ policies regarding harmful content and the technologies that create personalized experiences for users, which can sometimes recommend unlawful content and connections.

The ever-increasing amount of user-generated content online has led, in recent years, to an expansion in research and investment in automated content analysis tools. Scrutiny of automated content analysis has accelerated during the COVID-19 pandemic, as social networking services have placed a greater reliance on these tools due to concerns about health risks to their moderation staff from in-person work. At the same time, there are important policy debates around the world about how to improve content moderation while protecting free expression and privacy. In order to advance these debates, we need to understand the potential role of automated content analysis tools.

This paper explains the capabilities and limitations of tools for analyzing online multimedia content and highlights the potential risks of using these tools at scale without accounting for their limitations. It focuses on two main categories of tools: matching models and computer prediction models. Matching models include cryptographic and perceptual hashing, which compare user-generated content with existing and known content. Predictive models (including computer vision and computer audition) are machine learning techniques that aim to identify characteristics of new or previously unknown content.

The substantive focus of this article is a small collection of image-based case studies of significant criminal acts of interpersonal cruelty that are now in the public domain. In all instances those engaged in criminal acts of violence record and document aspects of their own behavior. The case studies range from military personnel and terrorists to examples from popular culture. Self-created images have the potential to serve as evidence of criminal behavior. For the viewing public recorded images are attributed with a “commonsense” evidential documentary potential. The article looks at the documentary method and the rationale behind recorded images serving evidential purposes.