A profile of arguing behaviors on Facebook

This study explored how people argue on social-networking sites. Specifically, participants (N = 170) responded to open and closed-ended questions about the most recent argument they had engaged in on Facebook. Results of a content analysis of participants' answers revealed individuals tended to argue mostly about public issues, in somewhat complex arguments that involved a median of six people and with about 30 comments exchanged. Individuals often pursued multiple goals, with persuasion and defending themselves or others also reported by some. Arguments tended to end without resolution, and most had no effects on arguers' relationships; however, for 20% of the sample, arguments permanently damaged their relationships. Although the number of friends participants had did not have a substantial effect on their frequency of arguing, the frequency with which one's friends argued on Facebook was positively related to one's own arguing frequency. These results are interpreted in connection to argumentation and computer-mediated-communication literature. Limitations of the study as well as directions for future research are also discussed

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

Control responsibility : the discursive construction of privacy, teens, and Facebook in Flemish newspapers

This study explores the discursive construction of online privacy through a critical discourse analysis of Flemish newspapers' coverage of privacy, teens, and Facebook between 2007 and 2018 to determine what representation of (young) users the papers articulate. A privacy-as-control discourse is dominant and complemented by two other discourses: that of the unconcerned and reckless teenager and that of the promise of media literacy. Combined, these discourses form an authoritative language on privacy that we call "control responsibility." Control responsibility presents privacy as an individual responsibility that can be controlled and needs to be learned by young users. We argue that the discourses contribute to a neoliberal rationality and have a disciplinary effect that strengthens various forms of responsibilization

Wild Westworld: Section 230 of the CDA and Social Networks’ Use of Machine-Learning Algorithms

This Note argues that Facebook’s services—specifically the personalization of content through machine-learning algorithms—constitute the “development” of content and as such do not qualify for § 230 immunity. This Note analyzes the evolution of § 230 jurisprudence to help inform the development of a revised framework. This framework is guided by congressional and public policy goals and creates brighter lines for technological immunity. It tailors immunity to account for user data mined by ISPs and the pervasive effect that the use of that data has on users—two issues that courts have yet to confront. This Note concludes that under the revised framework, machine-learning algorithms’ content organization— made effective through the collection of individualized data—make ISPs codevelopers of content and thus bar them from immunity

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

Student Privacy in Learning Analytics: An Information Ethics Perspective

In recent years, educational institutions have started using the tools of commercial data analytics in higher education. By gathering information about students as they navigate campus information systems, learning analytics “uses analytic techniques to help target instructional, curricular, and support resources” to examine student learning behaviors and change students’ learning environments. As a result, the information educators and educational institutions have at their disposal is no longer demarcated by course content and assessments, and old boundaries between information used for assessment and information about how students live and work are blurring. Our goal in this paper is to provide a systematic discussion of the ways in which privacy and learning analytics conflict and to provide a framework for understanding those conflicts. We argue that there are five crucial issues about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students’ privacy and associated rights, including (but not limited to) autonomy interests. First, we argue that we must distinguish among different entities with respect to whom students have, or lack, privacy. Second, we argue that we need clear criteria for what information may justifiably be collected in the name of learning analytics. Third, we need to address whether purported consequences of learning analytics (e.g., better learning outcomes) are justified and what the distributions of those consequences are. Fourth, we argue that regardless of how robust the benefits of learning analytics turn out to be, students have important autonomy interests in how information about them is collected. Finally, we argue that it is an open question whether the goods that justify higher education are advanced by learning analytics, or whether collection of information actually runs counter to those goods

Online Manipulation: Hidden Influences in a Digital World

Privacy and surveillance scholars increasingly worry that data collectors can use the information they gather about our behaviors, preferences, interests, incomes, and so on to manipulate us. Yet what it means, exactly, to manipulate someone, and how we might systematically distinguish cases of manipulation from other forms of influence—such as persuasion and coercion—has not been thoroughly enough explored in light of the unprecedented capacities that information technologies and digital media enable. In this paper, we develop a definition of manipulation that addresses these enhanced capacities, investigate how information technologies facilitate manipulative practices, and describe the harms—to individuals and to social institutions—that flow from such practices. We use the term “online manipulation” to highlight the particular class of manipulative practices enabled by a broad range of information technologies. We argue that at its core, manipulation is hidden influence—the covert subversion of another person’s decision-making power. We argue that information technology, for a number of reasons, makes engaging in manipulative practices significantly easier, and it makes the effects of such practices potentially more deeply debilitating. And we argue that by subverting another person’s decision-making power, manipulation undermines his or her autonomy. Given that respect for individual autonomy is a bedrock principle of liberal democracy, the threat of online manipulation is a cause for grave concern