A qualitative study of stakeholders' perspectives on the social network service environment

Over two billion people are using the Internet at present, assisted by the mediating activities of software agents which deal with the diversity and complexity of information. There are, however, ethical issues due to the monitoring-and-surveillance, data mining and autonomous nature of software agents. Considering the context, this study aims to comprehend stakeholders' perspectives on the social network service environment in order to identify the main considerations for the design of software agents in social network services in the near future. Twenty-one stakeholders, belonging to three key stakeholder groups, were recruited using a purposive sampling strategy for unstandardised semi-structured e-mail interviews. The interview data were analysed using a qualitative content analysis method. It was possible to identify three main considerations for the design of software agents in social network services, which were classified into the following categories: comprehensive understanding of users' perception of privacy, user type recognition algorithms for software agent development and existing software agents enhancement

Online advertising: analysis of privacy threats and protection approaches

Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft

Mind over machine? The clash of agency in social media environments

Includes bibliographical references.2022 Fall.Underlying many social media platforms are choice recommendation "nudging" architectures designed to give users instant content and social recommendations to keep them engaged. Powered by complex algorithms, these architectures flush people's feeds and an array of other features with fresh content and create a highly individualized experience tailored to their interests. In a critical realist qualitative study, this research examines how individual agency manifests when users encounter these tools and the suggestions they provide. In interviews and focus groups, 45 participants offered their experiences where they reflected on how they perceived the engines, e.g., their Facebook feed, influenced their actions and behaviors, as well as how the participants felt they controlled it to achieve personal aims. Based on these and other experiences, this study posits the Social Cognitive Machine Agency Dynamic (SCMAD) model, which provides an empirically supported explanatory framework to explain how individual agency can manifest and progress in response to these tools. The model integrates Albert Bandura's social cognitive theory concepts and emergent findings. It demonstrates how users react to the engines through agentic expressions not dissimilar to the real-world, including enacting self-regulatory, self-reflective and intentionality processes, as well as other acts not captured by Bandura's theory. Ultimately, the research and model propose a psycho-environmental explanation of the swerves of agency experienced by users in reaction to the unique conditions and affordances of these algorithmically driven environments. The study is the first known extension of social cognitive theory to this technology context. Implications of the findings are discussed and recommendations for future research provided. The study recommends that future research and media discourse aim for an individual-level psychological evaluation of these powerful technologies. This stance will afford a greater understanding of the technology's impacts and implications on individuals, particularly as it is anticipated to significantly evolve in the coming years

Platforms and the Fall of the Fourth Estate: Looking Beyond the First Amendment to Protect Watchdog Journalism

Journalists see the First Amendment as an amulet, and with good reason. It has long protected the Fourth Estate—an independent institutional press—in its exercise of editorial discretion to check government power. This protection helped the Fourth Estate flourish in the second half of the twentieth century and ably perform its constitutional watchdog role. But in the last two decades, the media ecology has changed. The Fourth Estate has been subsumed by a Networked Press in which journalists are joined by engineers, algorithms, audience, and other human and non-human actors in creating and distributing news. The Networked Press’s most powerful members are platforms. These platforms—companies like Facebook, Google, and Twitter—shun the media label even as they function as information gatekeepers and news editors. Their norms and values, including personalization and speed, stymie watchdog reporting. The Networked Press regime significantly threatens watchdog journalism, speech that is at the core of the press’s constitutional role. Yet, limited by the state action doctrine, the First Amendment cannot shield this speech from a threat by private actors like platforms. Today, the First Amendment is insufficient to protect a free press that can serve as a check on government tyranny. This article argues that we must look beyond the First Amendment to protect watchdog journalism from the corrosive power of platforms. It describes the limits of the First Amendment and precisely how platforms threaten watchdog journalism. It also proposes a menu of extra-constitutional options for bolstering this essential brand of speech

Digital Food Marketing to Children and Adolescents: Problematic Practices and Policy Interventions

Examines trends in digital marketing to youth that uses "immersive" techniques, social media, behavioral profiling, location targeting and mobile marketing, and neuroscience methods. Recommends principles for regulating inappropriate advertising to youth

PrivateRec: Differentially Private Training and Serving for Federated News Recommendation

Privacy protection is an essential issue in personalized news recommendation, and federated learning can potentially mitigate the privacy concern by training personalized news recommendation models over decentralized user data.For a theoretical privacy guarantee, differential privacy is necessary. However, applying differential privacy to federated recommendation training and serving conventionally suffers from the unsatisfactory trade-off between privacy and utility due to the high-dimensional characteristics of model gradients and hidden representations. In addition, there is no formal privacy guarantee for both training and serving in federated recommendation. In this paper, we propose a unified federated news recommendation method for effective and privacy-preserving model training and online serving with differential privacy guarantees. We first clarify the notion of differential privacy over users' behavior data for both model training and online serving in the federated recommendation scenario. Next, we propose a privacy-preserving online serving mechanism under this definition with differentially private user interest decomposition. More specifically, it decomposes the high-dimensional and privacy-sensitive user embedding into a combination of public basic vectors and adds noise to the combination coefficients. In this way, it can avoid the dimension curse and improve the utility by reducing the required noise intensity for differential privacy. Besides, we design a federated recommendation model training method with differential privacy, which can avoid the dimension-dependent noise for large models via label permutation and differentially private attention modules. Experiments on real-world news recommendation datasets validate the effectiveness of our method in achieving a good trade-off between privacy protection and utility for federated news recommendations

Shopping For Privacy: How Technology in Brick-and-Mortar Retail Stores Poses Privacy Risks for Shoppers

As technology continues to rapidly advance, the American legal system has failed to protect individual shoppers from the technology implemented into retail stores, which poses significant privacy risks but does not violate the law. In particular, I examine the technologies implemented into many brick-and-mortar stores today, many of which the average everyday shopper has no idea exists. This Article criticizes these technologies, suggesting that many, if not all of them, are questionable in their legality taking advantage of their status in a legal gray zone. Because the American judicial system cannot adequately protect the individual shopper from these questionable privacy practices, I call upon the Federal Trade Commission, the de facto privacy regulator in the United States, to increase its policing of physical retail stores to protect the shopper from any further harm