Using Artificial Intelligence and Machine Learning to Identify Terrorist Content Online

By MACDONALD, STUART KEITH, 1979-; MATTHEIS, ASHLEY A.; WELLS, DAVID

From the document: "Online terrorist propaganda has been an important policy concern for at least the past decade. [...] [T]he EU Commission launched a call for proposals for projects aimed at supporting small companies in implementing the Regulation. Three projects were funded under this call. This report forms part of one of these projects, which is entitled Tech Against Terrorism Europe. It is important to note at the outset that the focus of this report is the use of AI and machine learning to identify terrorist content online using content-based approaches. Accordingly, the following are outside the scope of the report: [1] The moderation of so-called borderline content, i.e., content that does not violate a platform's Terms of Service but which is nevertheless regarded as potentially harmful. [2] The identification of individuals on a radicalisation trajectory, which is a different - and even more difficult - task; and, [3] The use of behaviour-based cues, such as abnormal posting volume and use of unrelated, trending hashtags, to identify accounts that are sharing terrorist content. This includes approaches based on recidivism. The report begins, in section 2, by explaining the terms AI, machine learning and terrorist content online. Readers that are already familiar with these concepts may wish to move straight to section 3, which discusses the two main content-based approaches to the automated identification of terrorist content online: matching-based approaches and classification-based ones. Having explained the limitations of each approach, section 4 details two ways in which it is necessary to supplement automated tools. Section 5 then addresses issues of resource, before the report concludes with three recommendations."

TECH AGAINST TERRORISM; TECH AGAINST TERRORISM EUROPE.. 2023.. 32p.