Acknowledgments

A textbook about misinformation depends entirely on the vast community of researchers, journalists, fact-checkers, and educators who have built the evidence base it synthesizes. We are deeply grateful to all of them.

We owe particular intellectual debts to the researchers whose work appears throughout these pages: Daniel Kahneman and Amos Tversky for their foundational work on cognitive biases; Elizabeth Loftus for her decades of research on memory and the misinformation effect; Sander van der Linden and his colleagues for developing inoculation theory into a practical tool for the digital age; Kate Starbird, Kathleen Hall Jamieson, and the scholars at the Harvard Kennedy School Shorenstein Center for their rigorous empirical work on political misinformation; Sam Wineburg and the Stanford History Education Group for their groundbreaking research on how people actually evaluate online information; Renée DiResta, Alex Stamos, and the Stanford Internet Observatory for their documentation of information operations; and Naomi Oreskes and Erik Conway for "Merchants of Doubt," which remains the definitive account of how scientific consensus gets manufactured away.

The global fact-checking community — the International Fact-Checking Network and its member organizations including PolitiFact, FactCheck.org, Full Fact, Africa Check, AltNews, and hundreds more — do the unglamorous, essential work of checking one claim at a time against available evidence. Their methodology and integrity are an inspiration.

We are grateful to the open-source Python community whose tools make the computational chapters possible: the developers of scikit-learn, transformers (Hugging Face), NetworkX, pandas, matplotlib, NLTK, spaCy, and the dozens of other libraries that students in this course will learn to use.

This book was drafted with AI assistance and is published by DataField.Dev. Errors may remain. Instructors, students, researchers, and fact-checkers who find a mistake, a weak source, or a framing that deserves challenge are warmly invited to send corrections and suggestions through datafield.dev.

Finally, this book is for the students who will ask the hard questions and challenge its framings. That is exactly the habit it hopes to teach.

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