Part IV: Platform Case Studies

Algorithms Under the Microscope

From General to Specific

Parts I through III built the general argument: an economic logic (Part I) operating through specific biological mechanisms (Part II) via identifiable design patterns (Part III). That argument is necessary but not sufficient. General arguments can be true while specific implementations differ. A claim about how all platforms exploit dopamine reward systems does not automatically explain why TikTok feels different from Facebook, why YouTube produces different effects than Instagram, or why Snapchat's specific design choices produce the streak anxiety that Chapter 16 described in the abstract.

Part IV answers those questions. It takes the general argument and runs it through the specific — eight chapters examining how individual platforms have instantiated the engagement-maximization logic, what their particular design choices look like under the Part III taxonomy, and what we can learn from the differences as well as the similarities.

The case studies are not simply applications of prior chapters to new examples. They reveal things about the general argument that the general argument could not reveal on its own: that different platforms have made different choices within the same incentive structure; that some of those choices reflect different theories about which psychological mechanisms to prioritize; and that the differences between platforms tell us something important about design agency — about the space that existed for different choices.

The Python Components

Part IV introduces something that the earlier parts did not: code. Not as a substitute for conceptual analysis, but as a complement to it. Understanding how recommendation algorithms function at a conceptual level is one kind of knowledge. Seeing a simplified implementation — watching the mathematical logic that determines what a user sees — is another kind.

Chapters in this part include Python code components that model core algorithmic concepts: collaborative filtering, engagement-weighted ranking, reinforcement learning loops. These are simplified models, not production code; no one reading this book is getting access to TikTok's actual recommendation system. But working through the logic of a simplified model, even briefly, changes how the abstractions feel. When Chapter 23 describes TikTok's For You page as a continuous inference engine updating its model of your preferences in real time, the code component makes that description concrete in a way that description alone cannot fully achieve.

If you are not a programmer, do not skip these sections. The code is designed to be readable by anyone with patience and a willingness to follow the logic step by step. The conceptual commentary surrounding each code block explains what the code is doing and why. The mathematical details are less important than the structural insight.

The Arc of These Chapters

Chapter 22 provides the foundation: how recommendation algorithms work in general terms, before the platform-specific chapters get specific. Collaborative filtering, content-based filtering, reinforcement learning, and engagement optimization as objective function — Chapter 22 establishes the common vocabulary for what follows.

Chapter 23 examines TikTok's For You page — the most discussed and, in several respects, the most technically distinctive recommendation system in current social media. The FYP's speed of preference inference, its relative deweighting of social graph in favor of content signals, and its documented tendency toward increasingly narrow content clusters are all analyzed here.

Chapter 24 is the Facebook News Feed Arc — the running case study that has appeared in references throughout Parts I–III, now developed in full. This chapter traces the transition from chronological to algorithmic feed, the internal research on emotional contagion, the engagement-vs-wellbeing tradeoffs that Facebook's own data revealed, and what the documented decision-making process tells us about how engagement-maximization logic operates at the top of a company.

Chapter 25 turns to Instagram and the specific mechanism of social comparison — the visual grammar of the platform, the follower-count visibility structure, and the research on how Instagram's design produces comparison effects that differ from those of text-based platforms.

Chapter 26 examines YouTube's recommendation pipeline and the radicalization pathway — the documented tendency of the recommendation algorithm to push users toward progressively more extreme content within interest categories. This chapter addresses the algorithmic radicalization research carefully, acknowledging the contested elements while establishing what is well-supported.

Chapter 27 covers Snapchat's distinctive design choices: streaks, ephemerality, and the specific anxiety mechanics that Snapchat's design — almost uniquely among major platforms — produces through absence and impermanence rather than accumulation.

Chapter 28 analyzes gaming mechanics in social media — the deliberate importation of variable reward schedules, achievement systems, progression bars, and social leaderboards from the gaming industry into social platforms. This chapter connects to the broader literature on gamification and examines where that concept is analytically useful and where it is misleading.

Chapter 29 examines A/B testing as a practice — the methodology that underlies most major platform design decisions, and what it means when the optimization target is an engagement metric rather than a user wellbeing metric. This chapter is about epistemology as much as technology: what do platforms know about the effects of their design, when do they know it, and what do they do with that knowledge?

What the Differences Tell Us

One of the most important analytical payoffs of the case study approach is comparative. If all platforms were identical implementations of the same engagement-maximization logic, the differences between them would be trivial. But they are not identical. TikTok, Facebook, Instagram, and YouTube have made different choices about which psychological mechanisms to prioritize, how to design social proof signals, how aggressively to remove stopping cues, how transparent to be about recommendation logic.

Some of those differences reflect genuine values. Some reflect different theories about what produces long-term retention versus short-term engagement. Some reflect regulatory environments — platforms operating under stricter data protection laws have made different choices than those that have not. And some differences exist simply because different engineering teams made different decisions, within the same incentive structure, with different outcomes.

That last point is important for the book's overall argument. If the incentive structure fully determined the design, there would be no variation. The variation shows that the structure leaves room for choices — and that designers, product managers, and the people who set optimization objectives are genuine agents within that structure, not just conduits for inevitable outcomes.

That is not a comforting thought if you were hoping the incentive structure fully explains everything. But it is a necessary thought if you want to understand what reform could actually look like.

Maya and Velocity in the Algorithm

In Part IV, Maya's experience on specific platforms becomes the ground-level complement to the technical analysis. When Chapter 23 models TikTok's preference inference mechanism, Maya's experience of the FYP shifting from the content she initially sought to something she did not intend to find is the phenomenological accompaniment to the code.

Velocity Media, meanwhile, faces the concrete product decisions that Part I described in the abstract. In Part IV, Velocity's engineering team builds their recommendation system and discovers, in the choices they have to make about objective functions and optimization targets, exactly how the engagement-maximization incentive becomes embedded in code.