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A revealing and gripping investigation into how social media platforms police what we post online-and the large societal impact of these decisions Most users want their Twitter feed, Facebook page, and YouTube comments to be free of harassment and porn. Whether faced with "fake news" or livestreamed violence, "content moderators"-who censor or promote user-posted content-have never been more important. This is especially true when the tools that social media platforms use to curb trolling, ban hate speech, and censor pornography can also silence the speech you need to hear. In this revealing and nuanced exploration, award-winning sociologist and cultural observer Tarleton Gillespie provides an overview of current social media practices and explains the underlying rationales for how, when, and why these policies are enforced. In doing so, Gillespie highlights that content moderation receives too little public scrutiny even as it is shapes social norms and creates consequences for public discourse, cultural production, and the fabric of society. Based on interviews with content moderators, creators, and consumers, this accessible, timely book is a must-read for anyone who's ever clicked "like" or "retweet."

Proponents of generative AI tools claim they will supplement, even replace, the work of cultural production. This raises questions about the politics of visibility: what kinds of stories do these tools tend to generate, and what do they generally not? Do these tools match the kind of diversity of representation that marginalized populations and non-normative communities have fought to secure in publishing and broadcast media? I tested three widely available generative AI tools with prompts designed to reveal these normative assumptions; I prompted the tools multiple times with each, to track the diversity of the outputs to the same query. I demonstrate that, as currently designed and trained, generative AI tools tend to reproduce normative identities and narratives, rarely representing less common arrangements and perspectives. When they do generate variety, it is often narrow, maintaining deeper normative assumptions in what remains absent.

Recent social science concerning the information technology industries has been driven by a sense of urgency around the problems social media platforms face. But it need not be our job to solve the problems these industries have created, at least not on the terms in which they offer them. When researchers are enlisted in solving the industry's problems, we tend to repeat some of the missteps common to the study of technology and society.

Public debate about content moderation has overwhelmingly focused on removal: social media platforms deleting content and suspending users, or opting not to do so. However, removal is not the only available remedy. Reducing the visibility of problematic content is becoming a commonplace element of platform governance. Platforms use machine learning classifiers to identify content they judge misleading enough, risky enough, or offensive enough that, while it does not warrant removal according to the site guidelines, warrants demoting them in algorithmic rankings and recommendations. In this essay, I document this shift and explain how reduction works. I then raise questions about what it means to use recommendation as a means of content moderation.

AI seems like the perfect response to the growing challenges of content moderation on social media platforms: the immense scale of the data, the relentlessness of the violations, and the need for human judgments without wanting humans to have to make them. The push toward automated content moderation is often justified as a necessary response to the scale: the enormity of social media platforms like Facebook and YouTube stands as the reason why AI approaches are desirable, even inevitable. But even if we could effectively automate content moderation, it is not clear that we should.

Social media platforms don't just guide, distort, and facilitate social activity, they also delete some of it. They don't just link users together, they also suspend them. They don't just circulate our images and posts, they also algorithmically promote some over others. Platforms pick and choose.

Online content providers such as YouTube are carefully positioning themselves to users, clients, advertisers and policymakers, making strategic claims for what they do and do not do, and how their place in the information landscape should be understood. One term in particular, 'platform', reveals the contours of this discursive work. The term has been deployed in both their populist appeals and their marketing pitches, sometimes as technical 'platforms', sometimes as 'platforms' from which to speak, sometimes as 'platforms' of opportunity. Whatever tensions exist in serving all of these constituencies are carefully elided. The term also fits their efforts to shape information policy, where they seek protection for facilitating user expression, yet also seek limited liability for what those users say. As these providers become the curators of public discourse, we must examine the roles they aim to play, and the terms by which they hope to be judged.

Recently, the major US music and movie companies have pursued a dramatic renovation in their approach to copyright enforcement. This shift, from the 'code' of law to the 'code' of software, looks to technologies themselves to regulate or make unavailable those uses of content traditionally handled through law. Critics worry about the 'compliance' rules built into such systems: design mandates for manufacturers indicating what users can and cannot do under particular conditions. But these are accompanied by a second set of limitations: 'robustness' rules. Robustness rules obligate manufacturers to build devices such that they prevent tinkering - not only must the technology regulate its users, it must be inscrutable to them. This article examines this aspect of technical copyright regulation, looking particularly at the Content Scramble System (CSS) encryption system for DVDs and the recent 'broadcast flag' proposed for digital television. In the name of preventing piracy, these arrangements threaten to undermine users' sense of agency with their own technologies.

The term 'end-to-end' has become a familiar characterization of the architecture of the Internet, not only in engineering discourse, but in contexts as varied as political manifestos, commercial promotions, and legal arguments. Its ubiquity and opacity cloak the complexity of the technology it describes, and stand in for a richer controversy about the details of network design. This essay considers the appearance, in the 1970s, of the term 'end-to-end' in computer science discourse, and how the term became a point of contention within disputes about how to build a packet-switched network. I argue that the resolution of some of those disputes depended on the transformation of the term from descriptor to 'principle'. This transformation attempted to close specific design debates, and, in the process, made the term dramatically more useful in those discourses beyond engineering that eventually took a keen interest in the design of digital communication networks. The term, drawn from common parlance and given not only meaning but conviction, was shaped and polished so as to be mobile. As such, it actively managed and aligned disparate structural agendas, and has had subtle consequences for how the Internet has been understood, sold, legislated, and even re-designed.

Social media platforms have profoundly transformed cultural production, in part by restructuring the terms by which culture is distributed and paid for. In this article, we examine the YouTube Partner Program and the controversies around the "demonetization" of videos, to understand these arrangements and what happens when they shift beneath creators' feet. We use the testimony of YouTubers, provided in their own videos, to understand how creators square the contradiction between YouTube's increasingly cautious rules regarding "advertiser-friendly" content, its shifting financial and algorithmic incentive structure, and its stated values as an open platform of expression. We examine YouTube's tiered governance strategy, in which different users are offered different sets of rules, different material resources, and different procedural protections when content is demonetized. And we examine how, especially when the details of that tiered governance are ambiguous or poorly conveyed, creators develop their own theories for why their content has been demonetized—which can provide some creators a tactical opportunity to advance politically motivated accusations of bias against the platform.