In/Visible Bodies. On patients and privacy in a networked world

In the networked world, privacy and visibility become entangled in new and unexpected ways. This article uses the concept of networked visibility to explore the entanglement of technology and the visibility of patient bodies. Based\ud on semi-structured interviews with patients active in social media, this paper describes how multiple patient bodies are produced in the negotiations between the need for privacy and the need for social interaction. Information technology is actively involved in these negotiations: patients use technology to make their bodies both visible and invisible. At the same time technology collects data on these patients, which can be used for undesired commercial and surveillance\ud purposes. The notion of visibility by design may infuse design efforts that enable online privacy, supporting patients in the multiple ways they want to be visible and invisible online

The Future of “Fair and Balanced”: The Fairness Doctrine, Net Neutrality, and the Internet

In recent months, different groups--pundits, politicians, and even an FCC Commissioner--have discussed resurrecting the now-defunct Fairness Doctrine and applying it to Internet communication. This iBrief responds to the novel application of the Doctrine to the Internet in three parts. First, this iBrief will review the history and legal rationale that supported the Fairness Doctrine, with a particular emphasis on emerging technologies. Second, this iBrief applies these legal arguments to the evolving structure of the Internet. Third, this iBrief will consider what we can learn about Net Neutrality through an analogy to the Fairness Doctrine. This iBrief concludes that, while the Fairness Doctrine is not appropriate to use on the Internet in its present form, the arguments for the Doctrine could affect the debate surrounding Net Neutrality, depending on how the Obama Administration implements Net Neutrality

A literature review of the use of Web 2.0 tools in Higher Education

This review focuses on the use of Web 2.0 tools in Higher Education. It provides a synthesis of the research literature in the field and a series of illustrative examples of how these tools are being used in learning and teaching. It draws out the perceived benefits that these new technologies appear to offer, and highlights some of the challenges and issues surrounding their use. The review forms the basis for a HE Academy funded project, ‘Peals in the Cloud’, which is exploring how Web 2.0 tools can be used to support evidence-based practices in learning and teaching. The project has also produced two in-depth case studies, which are reported elsewhere (Galley et al., 2010, Alevizou et al., 2010). The case studies focus on evaluation of a recently developed site for learning and teaching, Cloudworks, which harnesses Web 2.0 functionality to facilitate the sharing and discussion of educational practice. The case studies aim to explore to what extent the Web 2.0 affordances of the site are successfully promoting the sharing of ideas, as well as scholarly reflections, on learning and teaching

Collaborative tagging : folksonomy, metadata, visualization, e-learning, thesis

Collaborative tagging is a simple and effective method for organizing and sharing web resources using human created metadata. It has arisen out of the need for an efficient method of personal organization, as the number of digital resources in everyday lives increases. While tagging has become a proven organization scheme through its popularity and widespread use on the Web, little is known about its implications and how it may effectively be applied in different situations. This is due to the fact that tagging has evolved through several iterations of use on social software websites, rather than through a scientific or an engineering design process. The research presented in this thesis, through investigations in the domain of e-learning, seeks to understand more about the scientific nature of collaborative tagging through a number of human subject studies. While broad in scope, touching on issues in human computer interaction, knowledge representation, Web system architecture, e-learning, metadata, and information visualization, this thesis focuses on how collaborative tagging can supplement the growing metadata requirements of e-learning. I conclude by looking at how the findings may be used in future research, through using information based in the emergent social networks of social software, to automatically adapt to the needs of individual users

The role of social media in negotiating identity during the process of acculturation

The purpose of this paper is to contribute to an understanding of the role of social media in negotiating and managing identity for transient migrants relating to the home and host culture during the acculturation process.Focussing on international students in the UK, this paper reports on findings from a qualitative study involving interviews with 27 transient migrants about their social media use and the negotiation of their identity online.This paper highlights the multifaceted role that social media plays in the identity negotiations of transient migrants and it offers three theoretical contributions. First, the authors show that social media serves as a medium, consequence and determinant of identity. Second, provide four strategies for identity management are provided: boundary management, access management, online content management and offline content management. Third, contextualised support is provided for a reciprocal relationship between the different identity-related roles played by social media

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

SEX, LABOR, AND DIGITAL SPACES: A CRITICAL DISCOURSE ANALYSIS OF GPGUIADELAS, A BRAZILIAN SEX WORKER TWITTER FEED

This dissertation project is a critical discourse analysis of written and visual texts produced for GPGuiaDelas, a Brazilian sex worker Twitter feed. Drawing on digital labor studies, feminist studies on sex work, and Brazilian studies on race and gender, 176 Twitter conversations between sex workers and clients were analyzed in order to answer the following: (1) What are the dominant themes in the discourse about sex work constructed through microblogging on social media?; (2) What are the discursive practices of sex workers who use social media as a platform?; and (3) What theoretical insights emerge from the analysis of sex workers’ discourse on social media? The analysis revealed three orders of discourse: economic, relational, and identity. The economic order of discourse highlights how sex workers are required to perform unpaid labor through digital media spaces. The relational order of discourse centers on the interpersonal affective labor sex workers execute. The identity order of discourse posits that sex workers appeal to available racial-sexual-gendered tropes within Brazil’s complex spectrum of racial, gender, and sexual ideologies in order to be legible and attract clients. This study also reveals how the architecture of Twitter enables discursive practices of power negotiation. I argue for forefronting sex workers’ voices in communication and media studies; blending theoretical lenses, and for giving close attention to the ways in which sex workers enact power within multiple systems of marginalization in Brazil. This study contributes to digital labor and media studies, communication studies, Brazilian studies, and feminist scholarship on sex work

Contributions to Robust Graph Clustering: Spectral Analysis and Algorithms

This dissertation details the design of fast, and parameter free, graph clustering methods to robustly determine set cluster assignments. It provides spectral analysis as well as algorithms that adapt the obtained theoretical results to the implementation of robust graph clustering techniques. Sparsity is of importance in graph clustering and a first contribution of the thesis is the definition of a sparse graph model consistent with the graph clustering objectives. This model is based on an advantageous property, arising from a block diagonal representation, of a matrix that promotes the density of connections within clusters and sparsity between them. Spectral analysis of the sparse graph model including the eigen-decomposition of the Laplacian matrix is conducted. The analysis of the Laplacian matrix is simplified by defining a vector that carries all the relevant information that is contained in the Laplacian matrix. The obtained spectral properties of sparse graphs are adapted to sparsity-aware clustering based on two methods that formulate the determination of the sparsity level as approximations to spectral properties of the sparse graph models. A second contribution of this thesis is to analyze the effects of outliers on graph clustering and to propose algorithms that address robustness and the level of sparsity jointly. The basis for this contribution is to specify fundamental outlier types that occur in the cases of extreme sparsity and the mathematical analysis of their effects on sparse graphs to develop graph clustering algorithms that are robust against the investigated outlier effects. Based on the obtained results, two different robust and sparsity-aware affinity matrix construction methods are proposed. Motivated by the outliers’ effects on eigenvectors, a robust Fiedler vector estimation and a robust spectral clustering methods are proposed. Finally, an outlier detection algorithm that is built upon the vertex degree is proposed and applied to gait analysis. The results of this thesis demonstrate the importance of jointly addressing robustness and the level of sparsity for graph clustering algorithms. Additionally, simplified Laplacian matrix analysis provides promising results to design graph construction methods that may be computed efficiently through the optimization in a vector space instead of the usually used matrix space