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The counterterrorism and CVE community has long questioned the effectiveness of counterspeech in countering extremism online. While most evaluation of counterspeech rely on limited reach and engagement metrics, this paper explores two models to better measure behavioral change and sentiment analysis. Conducted via partnerships between Facebook and counter-extremism NGOs, the first model uses A/B testing to analyze the effects of counterspeech exposure on low-prevalence-high-risk audiences engaging with Islamist extremist terrorist content. The second model builds upon online safety intervention approaches and the Redirect Method
through a search based “get-help” module, redirecting whitesupremacy and Neo-Nazi related search-terms to disengagement NGOs.

Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset. Our contribution is twofold: 1) creating a granular taxonomy for hateful online comments that includes both types and targets of hateful comments, and 2) experimenting with machine learning, including Logistic Regression, Decision Tree, Random Forest, Adaboost, and Linear SVM, to generate a multiclass, multilabel classification model that automatically detects and categorizes hateful comments in the context of online news media. We find that the best performing model is Linear SVM, with an average F1 score of 0.79 using TF-IDF features. We validate the model by testing its predictive ability, and, relatedly, provide insights on distinct types of hate speech taking place on social media.

This dissertation explores the various online radicalization and recruitment practices of groups like al-Qaeda and Hezbollah, as well as Salafi Jihadists in general. I will also outline the inadequacies of the federal government’s engagement with terrorist / Islamist ideologies and explore the ways in which early 20th century foundational Islamist theorists like Hasan al-Banna, Sayyid Qutb, and Abul ala Mawdudi have affected contemporary extremist Islamist groups, while exploring this myth of the ideal caliphate which persists in the ideology of contemporary extremist Islamist groups. In a larger sense, I am arguing that exploitation of the internet (particularly social networking platforms) in the radicalization of new communities of followers is much more dangerous than cyberterrorism (as in attacks on cyber networks within the government and the private sector), which is what is most often considered to be the primary threat that terrorists pose with their presence on the internet. Online radicalization should, I argue, be given more consideration when forming public policy because of the immediate danger that it poses, especially given the rise of microterrorism. Similarly, through the case studies that I am examining, I am bringing the humanities into the discussion of extremist (religious) rhetorics, an area of discourse that those scholars have largely ignored.

This article considers the terrorism financing risk associated with the growth of Financial Technology (FinTech) innovations and in particular, focuses on virtual currency (VC) products and services. The ease with which cross-border payments by virtual currencies are facilitated, the anonymity surrounding their usage and their potential to be converted into the fiat financial system, make them ideal for terrorism financing and therefore calls for a coordinated global regulatory response. This article considers the extent of the risk of terrorism financing through virtual currencies in ‘high risk’ States by focusing on countries that have been recently associated with terrorism activities. It assesses the robustness of their financial regulatory and law enforcement regimes in combating terrorism financing and considers the extent to which Regulatory Technology (RegTech) and its global standardisation, can mitigate this risk.

This thesis examines extremist Muslim narratives, discourse, and ideologies over the internet by using content analysis to thematically delineate and reconstruct them for the purpose of discovering the argumentation mechanisms through which they become persuasive and appealing. The research problem is that dominant theories in social sciences and popular literature create ‘taken for granted’ inferences that relegate extremist ideologies and narratives to the realm of structural contingencies, psychological pathologies, emotive appeal, manipulated religious ideologies, peculiar and unique rationalities or group dynamics. This thesis hypothesizes instead of the existence of a `logical structure` in extremist Muslim narratives. This logical structure is predicated on rationally persuasive arguments (which employ epistemic and instrumental rationality coupled with inductive/deductive reasoning) that appeal to any rational individual but are ultimately leveraged on for morally wrong end-state choices. Unfortunately much of the counter-narratives today seldom address this logical structure and choose to address the more traditional explanations cited above. Themes and argumentation mechanisms stemming from an examination of extremist Muslim narratives in this study demonstrate the presence and workings of this logical structure.

From its start, the so-called Islamic State of Iraq and the Levant (ISIL/ISIS) has been successfully exploiting social media networks, most notoriously Twitter, to promote its propaganda and recruit new members, resulting in thousands of social media users adopting pro-ISIS stance every year. Automatic identification of pro-ISIS users on social media has, thus, become the centre of interest for various governmental and research organisations. In this paper we propose a semantic-based approach for radicalisation detection on Twitter. Unlike most previous works, which mainly rely on the lexical and contextual representation of the content published by Twitter users, our approach extracts and makes use of the underlying semantics of words exhibited by these users to identify their pro/anti-ISIS stances. Our results show that classifiers trained from words’ semantics outperform those trained from lexical and network features by 2% on average F1-measure.