Corporate Social Responsibility and Social Media Corporations: Incorporating Human Rights Through Rankings, Self-Regulation and Shareholder Resolutions

This article examines the emergence and evolution of selected ranking and reporting frameworks in the expanding realm of business and human rights advocacy. It explores how indicators in the form of rankings and reports evaluating the conduct of transnational corporate actors can serve as regulatory tools with potential to bridge a global governance gap that often places human rights at risk. Specifically, this article examines the relationship of transnational corporations in the Internet communications technology sector (ICT sector) to human rights and the risks presented to the right to freedom of expression and the right to privacy when ICT sector companies comply with government demands to disclose user data or to conceal information users seek. Specifically, it explores the controversial role of transnational ICT corporations in state censorship and surveillance practices. The article explains how conflicts over corporate complicity in alleged abuses served to catalyze change and lead to the creation of the Global Network Initiative, a private multi-stakeholder project, and the Ranking Digital Rights Initiative, an industry independent market-based information effort. Both aim to promote more responsible business practices in the social media industry sector. In conclusion, the article argues that regulating corporate reporting of information relevant to assessing the potential for adverse human rights impacts is necessary

The Digital Architectures of Social Media: Comparing Political Campaigning on Facebook, Twitter, Instagram, and Snapchat in the 2016 U.S. Election

The present study argues that political communication on social media is mediated by a platform's digital architecture, defined as the technical protocols that enable, constrain, and shape user behavior in a virtual space. A framework for understanding digital architectures is introduced, and four platforms (Facebook, Twitter, Instagram, and Snapchat) are compared along the typology. Using the 2016 US election as a case, interviews with three Republican digital strategists are combined with social media data to qualify the studyies theoretical claim that a platform's network structure, functionality, algorithmic filtering, and datafication model affect political campaign strategy on social media

Report to the Childhood Development Initiative on Archiving of C.D.I. Data

This report presents the ethical and legal issues involved in depositing data-sets of research for secondary use in Ireland

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval

Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions

Contingencies of Self-Worth and Social-Networking-Site Behavior

Social-networking sites like Facebook enable people to share a range of personal information with expansive groups of "friends." With the growing popularity of media sharing online, many questions remain regarding antecedent conditions for this behavior. Contingencies of self-worth afford a more nuanced approach to variable traits that affect self-esteem, and may help explain online behavior. A total of 311 participants completed an online survey measuring such contingencies and typical behaviors on Facebook. First, exploratory factor analyses revealed an underlying structure to the seven dimensions of self-worth. Public-based contingencies explained online photo sharing (beta = 0.158, p < 0.01), while private-based contingencies demonstrated a negative relationship with time online (beta = -0.186, p < 0.001). Finally, the appearance contingency for self-worth had the strongest relationship with the intensity of online photo sharing (beta = 0.242), although no relationship was evident for time spent managing profiles.Radio-Television-Fil