ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Filtering by
- All Subjects: Social Media
- Language: English
Through a close reading of the discourses surrounding these popular social media platforms and a rhetorical analysis of their technological affordances, I documented the transference of gender-biased assumptions about women's roles, interests, and competencies, which have historically been found in face-to-face contexts, to these digital spaces. For example, cultural assumptions about the frivolity of women's interests, endeavors, issues, and labors make their way into digital discourse that situates the online practices of women as those of passive consumers who use the internet only to shop and socialize, rather than to go about the serious, masculine business of making original digital content.
This project expands on existing digital identity and performativity research, while applying a sorely needed feminist critique to online discourses and discursive practices that assume maleness and masculinity as the default positionality. These methods are one approach to addressing the pressing problems of online harassment, the gender gap in the technology sector, and the gender gap in digital literacies that have pedagogical, political, and structural implications for the classroom, workplace, economic markets, and civic sphere.
Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.