Matching Items (4)
Filtering by
- All Subjects: Computer Science
- All Subjects: Radicalization
- All Subjects: Terrorism--Religious aspects--Islam--Social aspects.
- Genre: Doctoral Dissertation
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
Stock market news and investing tips are popular topics in Twitter. In this dissertation, first I utilize a 5-year financial news corpus comprising over 50,000 articles collected from the NASDAQ website matching the 30 stock symbols in Dow Jones Index (DJI) to train a directional stock price prediction system based on news content. Next, I proceed to show that information in articles indicated by breaking Tweet volumes leads to a statistically significant boost in the hourly directional prediction accuracies for the DJI stock prices mentioned in these articles. Secondly, I show that using document-level sentiment extraction does not yield a statistically significant boost in the directional predictive accuracies in the presence of other 1-gram keyword features. Thirdly I test the performance of the system on several time-frames and identify the 4 hour time-frame for both the price charts and for Tweet breakout detection as the best time-frame combination. Finally, I develop a set of price momentum based trade exit rules to cut losing trades early and to allow the winning trades run longer. I show that the Tweet volume breakout based trading system with the price momentum based exit rules not only improves the winning accuracy and the return on investment, but it also lowers the maximum drawdown and achieves the highest overall return over maximum drawdown.
ContributorsAlostad, Hana (Author) / Davulcu, Hasan (Thesis advisor) / Corman, Steven (Committee member) / Tong, Hanghang (Committee member) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2016
Description
Social networking platforms have redefined communication, serving as conduits forswift global information dissemination on contemporary topics and trends. This research
probes information cascade (IC) dynamics, focusing on viral IC, where user-shared information
gains rapid, widespread attention. Implications of IC span advertising, persuasion,
opinion-shaping, and crisis response.
First, this dissertation aims to unravel the context behind viral content, particularly in
the realm of the digital world, introducing a semi-supervised taxonomy induction framework
(STIF). STIF employs state-of-the-art term representation, topical phrase detection,
and clustering to organize terms into a two-level topic taxonomy. Social scientists then
assess the topic clusters for coherence and completeness. STIF proves effective, significantly
reducing human coding efforts (up to 74%) while accurately inducing taxonomies
and term-to-topic mappings due to the high purity of its topics. Second, to profile the
drivers of virality, this study investigates messaging strategies influencing message virality.
Three content-based hypotheses are formulated and tested, demonstrating that incorporation
of “negativity bias,” “causal arguments,” and “threats to personal or societal core
values” - singularly and jointly - significantly enhances message virality on social media,
quantified by retweet counts. Furthermore, the study highlights framing narratives’ pivotal
role in shaping discourse, particularly in adversarial campaigns. An innovative pipeline
for automatic framing detection is introduced, and tested on a collection of texts on the
Russia-Ukraine conflict. Integrating representation learning, overlapping graph-clustering,
and a unique Topic Actor Graph (TAG) synthesis method, the study achieves remarkable
framing detection accuracy. The developed scoring mechanism maps sentences to automatically
detect framing signatures. This pipeline attains an impressive F1 score of 92%
and a 95% weighted accuracy for framing detection on a real-world dataset.
In essence, this dissertation focuses on the multidimensional exploration of information cascade, uncovering the context and drivers of content virality, and automating framing detection.
Through innovative methodologies like STIF, messaging strategy analysis, and
TAG Frames, the research contributes valuable insights into the mechanics of viral content
spread and framing nuances within the digital landscape, enriching fields such as advertisement,
communication, public discourse, and crisis response strategies.
ContributorsMousavi, Maryam (Author) / Davulcu, Hasan HD (Thesis advisor) / Li, Baoxin (Committee member) / Corman, Steven (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2023