Matching Items (3)
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- All Subjects: Machine Learning
- All Subjects: Radicalization
- All Subjects: Terrorism--Religious aspects--Islam--Social aspects.
- Genre: Doctoral Dissertation
- Resource Type: Text
Description
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.
ContributorsSalihu, Flurije (Author) / Ali, Souad T. (Thesis advisor) / Miller, Keith (Thesis advisor) / Corman, Steven (Committee member) / Gee, James P (Committee member) / Arizona State University (Publisher)
Created2014
Description
This research start utilizing an efficient sparse inverse covariance matrix (precision matrix) estimation technique to identify a set of highly correlated discriminative perspectives between radical and counter-radical groups. A ranking system has been developed that utilizes ranked perspectives to map Islamic organizations on a set of socio-cultural, political and behavioral scales based on their web site corpus. Simultaneously, a gold standard ranking of these organizations was created through domain experts and compute expert-to-expert agreements and present experimental results comparing the performance of the QUIC based scaling system to another baseline method for organizations. The QUIC based algorithm not only outperforms the baseline methods, but it is also the only system that consistently performs at area expert-level accuracies for all scales. Also, a multi-scale ideological model has been developed and it investigates the correlates of Islamic extremism in Indonesia, Nigeria and UK. This analysis demonstrate that violence does not correlate strongly with broad Muslim theological or sectarian orientations; it shows that religious diversity intolerance is the only consistent and statistically significant ideological correlate of Islamic extremism in these countries, alongside desire for political change in UK and Indonesia, and social change in Nigeria. Next, dynamic issues and communities tracking system based on NMF(Non-negative Matrix Factorization) co-clustering algorithm has been built to better understand the dynamics of virtual communities. The system used between Iran and Saudi Arabia to build and apply a multi-party agent-based model that can demonstrate the role of wedges and spoilers in a complex environment where coalitions are dynamic. Lastly, a visual intelligence platform for tracking the diffusion of online social movements has been developed called LookingGlass to track the geographical footprint, shifting positions and flows of individuals, topics and perspectives between groups. The algorithm utilize large amounts of text collected from a wide variety of organizations’ media outlets to discover their hotly debated topics, and their discriminative perspectives voiced by opposing camps organized into multiple scales. Discriminating perspectives is utilized to classify and map individual Tweeter’s message content to social movements based on the perspectives expressed in their tweets.
ContributorsKim, Nyunsu (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Hsiao, Sharon (Committee member) / Corman, Steven (Committee member) / Arizona State University (Publisher)
Created2018
Description
Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques require data to train the algorithms and model a complex system and make predictions based on that model. Due to development of sophisticated sensors it has become easier to collect large volumes of data which is used to make necessary hypotheses using ML. The promising results obtained using ML have opened up new opportunities of research across various departments and this dissertation is a manifestation of it. Here, some unique studies have been presented, from which valuable inference have been drawn for a real-world complex system. Each study has its own unique sets of motivation and relevance to the real world. An ensemble of signal processing (SP) and ML techniques have been explored in each study. This dissertation provides the detailed systematic approach and discusses the results achieved in each study. Valuable inferences drawn from each study play a vital role in areas of science and technology, and it is worth further investigation. This dissertation also provides a set of useful SP and ML tools for researchers in various fields of interest.
ContributorsDutta, Arindam (Author) / Bliss, Daniel W (Thesis advisor) / Berisha, Visar (Committee member) / Richmond, Christ (Committee member) / Corman, Steven (Committee member) / Arizona State University (Publisher)
Created2018