Two theoretical approaches have recently emerged to characterize new digital objects of study in the media landscape: infrastructure studies and platform studies. Despite their separate origins and different features, we demonstrate in this article how the cross-articulation of these two perspectives improves our understanding of current digital media. We use case studies of the Open Web, Facebook, and Google to demonstrate that infrastructure studies provides a valuable approach to the evolution of shared, widely accessible systems and services of the type often provided or regulated by governments in the public interest. On the other hand, platform studies captures how communication and expression are both enabled and constrained by new digital systems and new media. In these environments, platform-based services acquire characteristics of infrastructure, while both new and existing infrastructures are built or reorganized on the logic of platforms. We conclude by underlining the potential of this combined framework for future case studies.

Our daily digital life is full of algorithmically selected content such as social media feeds, recommendations and personalized search results. These algorithms have great power to shape users' experiences, yet users are often unaware of their presence. Whether it is useful to give users insight into these algorithms' existence or functionality and how such insight might affect their experience are open questions. To address them, we conducted a user study with 40 Facebook users to examine their perceptions of the Facebook News Feed curation algorithm. Surprisingly, more than half of the participants (62.5%) were not aware of the News Feed curation algorithm's existence at all. Initial reactions for these previously unaware participants were surprise and anger. We developed a system, FeedVis, to reveal the difference between the algorithmically curated and an unadulterated News Feed to users, and used it to study how users perceive this difference. Participants were most upset when close friends and family were not shown in their feeds. We also found participants often attributed missing stories to their friends' decisions to exclude them rather than to Facebook News Feed algorithm. By the end of the study, however, participants were mostly satisfied with the content on their feeds. Following up with participants two to six months after the study, we found that for most, satisfaction levels remained similar before and after becoming aware of the algorithm's presence, however, algorithmic awareness led to more active engagement with Facebook and bolstered overall feelings of control on the site.

History repeatedly demonstrates that rural communities have unique technological needs. Yet little is known about how rural communities use modern technologies, which therefore results in a collective lack knowledge about how to design for rural life. To address this gap, the present empirical article investigates behavioral differences between more than 3,000 rural and urban social media users. With a data set collected from a broadly popular social network site, the current work analyzes users’ profiles, 340,000 online friendships, and 200,000 interpersonal messages. Based on social capital theory, differences are predicted between rural and urban users, and strong evidence supports the present hypotheses—namely, rural people articulate far fewer friends online, and those friends live much closer to home. Results indicate that the groups have substantially different gender distributions and use privacy features differently. The article concludes with a discussion of the design implications drawn from these findings; most important, designers should reconsider the binary friend-or-not model to allow for incremental trust building.

No abstract

No abstract