Chapter 8 Further Reading: Algorithm Literacy

1. "The Spread of True and False News Online" — Soroush Vosoughi, Deb Roy, Sinan Aral (Science, 2018)

The landmark MIT Media Lab study demonstrating that false news spreads faster, farther, and more broadly than accurate news on Twitter — primarily because false news is more emotionally novel and generates more surprise and disgust (which drive sharing). This paper is essential reading for understanding why engagement-optimized algorithms inadvertently amplify misinformation: they reward novelty and emotional intensity, and false stories tend to be more novel and emotionally intense than true ones. The implications extend well beyond Twitter to any platform optimizing for engagement.

Available free through MIT's DSpace repository. Approximately 4,500 words plus supplementary materials.

2. "Invisible Censorship: How TikTok Moderates Marginalized Content" — The Intercept (2020)

An investigative report based on leaked TikTok moderation documents revealing that the company instructed moderators to suppress content from creators deemed "too ugly," "too poor," or "too disabled" — ostensibly to prevent harassment, but with effects that systematically depressed content from marginalized creators. The piece raises critical questions about where platform safety policies end and discriminatory suppression begins. Read alongside the TikTok company's response (available at the original URL) to understand the contested nature of these claims. Approximately 3,000 words.

3. "How YouTube's Algorithm Really Works" — YouTube Creator Academy

YouTube's own educational resource on how its recommendation system operates, available at creatoracademy.youtube.com. While deliberately incomplete (YouTube does not reveal its full algorithm), this resource provides the most authoritative available account of which signals YouTube acknowledges weighing, how its CTR/watch time relationship works, and how channels can improve their performance. Read critically — note what it addresses, and pay attention to what it conspicuously omits. Free.

4. "Algorithms of Oppression: How Search Engines Reinforce Racism" — Safiya Umoja Noble (NYU Press, 2018)

While focused on search rather than recommendation algorithms, Noble's foundational book builds the theoretical framework for understanding how seemingly neutral technological systems reproduce and amplify existing social inequalities. Her analysis of how algorithmic systems encode the biases of their creators is essential context for understanding the documented disparate impacts of social media recommendation algorithms. Chapter 1 and Chapter 5 are most directly relevant to this chapter's content. Available in most university libraries.

5. "Twitter's Algorithm Open-Source Release: What We Learned" — The Verge (April 2023)

When Elon Musk's team open-sourced portions of Twitter/X's recommendation algorithm in 2023, journalists and researchers analyzed the code to identify what signals the system actually weighted. This analysis revealed the specific metrics Twitter weighted for its "For You" algorithm, including the preferential treatment given to verified (paid) accounts and the algorithmic elevation of Musk's own account. A case study in what we learn when algorithm black boxes are (partially) opened. Approximately 2,500 words.

6. "Invisible Influence: The Hidden Forces That Shape Behavior" — Jonah Berger (Simon & Schuster, 2016)

Berger's research on what makes content shareable — specifically his STEPPS framework (Social Currency, Triggers, Emotion, Public, Practical Value, Stories) — provides a behavioral science foundation for understanding why certain content generates high-weight signals like shares and saves. Understanding the psychology of sharing is the complement to understanding the mechanics of algorithms: the algorithm rewards what humans share; Berger explains why humans share what they do. Chapters 3 (Emotion) and 5 (Practical Value) are most directly applicable.

7. "The Filter Bubble: What the Internet Is Hiding From You" — Eli Pariser (Penguin Press, 2011)

The book that coined the term "filter bubble" — the phenomenon of algorithmic personalization progressively narrowing the range of information users encounter. While published before TikTok existed and using early Facebook as its primary example, Pariser's core thesis has been repeatedly validated by subsequent research. Reading this book helps you understand the systemic consequence of recommendation algorithms at a societal level, beyond the creator-optimization frame. Chapter 1 and Chapter 5 are the essential sections.

8. "How TikTok's Algorithm Figures You Out" — Wall Street Journal (2021)

An interactive feature in which WSJ journalists created test accounts and documented exactly how TikTok's algorithm classified their interests and changed their feeds over time. The journalists' accounts were deliberately given different behavioral signals, and the report documents how quickly and accurately the algorithm identified their interests — including some they had not explicitly expressed. One of the clearest public demonstrations of how TikTok's FYP actually operates in practice. Available online; interactive elements may require subscription.

9. "Creator Earnings: YouTube's 2023 Report" — YouTube

YouTube periodically releases aggregated data about creator earnings and the relationship between channel size and revenue. This report (and similar ones from other platforms) provides real data on the distribution of earnings — helping you understand how the algorithm's distribution of reach translates into the distribution of economic opportunity. Note that these reports are produced by the platforms themselves and should be read with awareness of their interest in presenting favorable narratives about creator earnings.

10. "The Chaos Machine: The Inside Story of How Social Media Rewired Our Minds and Our World" — Max Fisher (Little, Brown, 2022)

A journalist's account of how engagement-optimized algorithms reshaped political discourse, mental health, and civil society globally. Fisher's reporting on how Facebook's recommendation algorithm amplified political extremism in Myanmar, the Philippines, and the United States is among the most important journalism about algorithmic systems produced in the 2020s. Relevant to this chapter's equity callout and to anyone who wants to understand the macro-level consequences of the systems individual creators participate in.

11. "How to Get 100 Million Views on YouTube" — Mark Rober (MrBeast's Creative Director's insights)

Not a formal publication, but the series of interviews with MrBeast, his editors, and his creative director in which they discuss their systematic approach to thumbnail testing, retention engineering, and concept development. Available through various YouTube creator interviews and podcast appearances (search for MrBeast creator process interviews 2022–2025). This is the primary source documentation of the approach described in Case Study 8.2 and illustrates algorithmic optimization at its most extreme and intentional form.

12. "Equity in Creator Monetization" — Creator Economy Research Institute (2024)

An annual report tracking earnings disparities across creator demographics on major platforms. Covers differential brand deal rates, algorithmic reach disparities, and platform monetization feature access across race, gender, and geography. Essential reading for anyone wanting to understand the equity dimensions of the creator economy in quantitative terms rather than anecdote. Available as a free download at creatoreconomyresearch.org.