Prerequisites

The Short Answer

None. This textbook requires no prior academic background in psychology, neuroscience, computer science, or any other field. It was written to be accessible to anyone who has used social media and wondered how it works.

What Will Help (But Isn't Required)

Basic Familiarity with Social Media

You will get more from this book if you have actually used social media platforms — particularly TikTok, Instagram, Facebook, or YouTube. The book's examples will be more vivid if you have experienced the mechanisms it describes firsthand. That said, the concepts are explained clearly enough that a non-user can follow the analysis.

Openness to Empirical Evidence

Several chapters engage with research findings that may challenge strongly-held views — both the view that social media is uniformly harmful and the view that any concern is overblown. The book works best for readers who are willing to follow evidence even when it complicates a preferred narrative.

Comfort with Uncertainty

Many of the debates covered in this book are not resolved. We do not pretend they are. Readers who need clear, simple answers may find some chapters frustrating. Readers who can sit with "it depends, and here's how it depends" will find the book satisfying.

For the Code Chapters (20, 22–29)

Chapters 20 and 22–29 include Python code. You do not need to understand the code to understand the chapters. Every code block is explained in plain English, and the conceptual content does not depend on the implementation details.

If you want to run the code: - Python: Version 3.10 or higher - Libraries: numpy, pandas, scikit-learn, matplotlib (install via pip install numpy pandas scikit-learn matplotlib) - No prior programming experience required to follow the code walkthrough explanations

For readers with a computer science background: the code in these chapters is written for clarity and teachability, not performance or production use. It deliberately trades elegance for explainability.

For the Statistics-Adjacent Content

Several chapters reference statistical findings — effect sizes, confidence intervals, p-values, correlation vs. causation. You do not need to know statistics to understand the arguments. But if you find yourself wanting to understand the evidence more deeply, Appendix A (Research Methods Primer) provides a plain-language introduction to the key concepts.

Conceptual Prerequisites by Part

A Note on Jargon

Every technical term is defined when first introduced in the text, and all key terms appear in the Glossary (appendices/glossary.md). If you encounter an unfamiliar term, check the Glossary — it will include the chapter where the term was first defined, so you can go back to the original context if needed.

We use technical terms not to signal expertise but because they carry precise meanings that resist substitution. "Variable ratio reinforcement schedule" is not jargon for its own sake — it means something specific that "random reward" does not capture. We define it, use it consistently, and expect you to add it to your vocabulary.

If you're uncertain whether this book is right for you: read the preface and the first three sections of Chapter 1. If those make sense and feel worth continuing, you have what it takes to complete the book.