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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.

What explains the rise and fall of far-right Islamist protest mobilization in contemporary Indonesia? The 2016-2017 Jakarta gubernatorial election witnessed significant growth in support for and mobilization of the far-right. While far-right Islamist mobilization has occurred regularly since the fall of the authoritarian Suharto regime in 1998, its longevity and impact on electoral politics has historically been limited. I maintain that the 2016 far-right Islamist protest mobilization was enabled and disabled by the dynamic relationship between hybrid media and hybrid movement strategies. Hybrid thinking serves as a platform for understanding the increasingly diverse and complex nature of Islamist mobilization, generating new ways of exploring some of the classic concerns of social movement studies and technology. Moreover, previous research on the far-right primarily focuses on electoral and party politics, while studies on social movements and technologies often fall into technological determinism, providing only a limited understanding of one of the most pressing issues of our time. This study proposes an innovative analytic strategy to understand the complexity of contemporary Islamist protest mobilization, creating a hybrid movement that effectively spans different movements, constituencies, and institutions, all coexisting within a hybrid media environment that deftly merges elements of old and newer media logics to influence participation, collaboration, and coordination in the spheres of social movements and protest politics.

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.

VOX-Pol Guest Lecture Series, Spring 2023

Speaker: Dr. Erin Saltman

Title: Cross-Platform Approaches to Countering Terrorism and Violent Extremism Online

Outline: This presentation reviews advances in cross-platform solutions advanced through the Global Internet Forum to Counter Terrorism (GIFCT). GIFCT was founded in 2017 by technology platforms to give structure and support for countering terrorism and violent extremism online. This includes workstreams for (1) multi stakeholder knowledge-sharing, (2) developing action-oriented research, (3) building cross-platform tech solutions, and (4) managing shared incident response protocols. The presentation reviews the state of play in adversarial shifts by violent extremist groups online and what this means for the evolution of counter measures in the online space. It is followed up by a Q&A covering topics such as transparency, the technical aspects of the Hash Sharing Database, and the ethics of funding.

Approaches for strategically countering or providing alternatives to hate speech and extremism online have evolved substantively in the last ten years. Technological advancement and a generation of young activists who have been socialized as digital natives have facilitated a maelstrom of both hate-based extremist content and attempts to counter this material in different guises and through diverse channels. The rate and pace of change within the tech sector, and social media growth in particular, have meant that although counterspeech is now more prevalent than ever before, it requires greater guidance and more robust public–private partnerships to effectively prevent and counter extremism online. The chapter embraces a cross-platform and international overview of some of the best practices within efforts to prevent and counter violent extremism online and discusses the future of counterspeech with recommendations for expanded innovation and partnership models.

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 white-supremacy and Neo-Nazi related search-terms to disengagement NGOs.