How Algorithms Create and Prevent Fake News : Exploring the Impacts of Social Media, Deepfakes, GPT-3, and More

"It's a joy to read a book by a mathematician who knows how to write. [...] There is no better guide to the strategies and stakes of this battle for the future." —-Paul Romer, Nobel Laureate, University Professor in Economics at NYU, and former Chief Economist at the World Bank. "By explaining the flaws and foibles of everything from Google search to QAnon—and by providing level-headed evaluations of efforts to fix them—Noah Giansiracusa offers the perfect starting point for anyone entering the maze of modern digital media." — Jonathan Rauch, senior fellow at the Brookings Institute and contributing editor of The Atlantic From deepfakes to GPT-3, deep learning is now powering a new assault on our ability to tell what's real and what's not, bringing a whole new algorithmic side to fake news. On the other hand, remarkable methods are being developed to help automate fact-checking and the detection of fake news and doctored media. Success in the modern business world requires you to understand these algorithmic currents, and to recognize the strengths, limits, and impacts of deep learning—-especially when it comes to discerning the truth and differentiating fact from fiction. This book tells the stories of this algorithmic battle for the truth and how it impacts individuals and society at large. In doing so, it weaves together the human stories and what's at stake here, a simplified technical background on how these algorithms work, and an accessible survey of the research literature exploring these various topics. How Algorithms Create and Prevent Fake News is an accessible, broad account of the various ways that data-driven algorithms have been distorting reality and rendering the truth harder to grasp. From news aggregators to Google searches to YouTube recommendations to Facebook news feeds, the way we obtain information today is filtered through the lens of tech giant algorithms. The way data is collected, labelled, and stored has a big impact on the machine learning algorithms that are trained on it, and this is a main source of algorithmic bias ­– which gets amplified in harmful data feedback loops. Don't be afraid: with this book you'll see the remedies and technical solutions that are being applied to oppose these harmful trends. There is hope. What You Will Learn The ways that data labeling and storage impact machine learning and how feedback loops can occur The history and inner-workings of YouTube's recommendation algorithm The state-of-the-art capabilities of AI-powered text generation (GPT-3) and video synthesis/doctoring (deepfakes) and how these technologies have been used so far The algorithmic tools available to help with automated fact-checking and truth-detection Who This Book is For People who don't have a technical background (in data, computers, etc.) but who would like to learn how algorithms impact society; business leaders who want to know the powers and perils of relying on artificial intelligence. A secondary audience is people with a technical background who want to explore the larger social and societal impact of their work. Contents About the Author Acknowledgments Introduction Chapter 1: Perils of Pageview Propagation of Stories Economics of Blogging Up from the Bottom Historical Context Examples of Fake News Peddlers Losing Reliable Local News Summary Chapter 2: Crafted by Computer Synthetic Photos Automated Headlines Writing Entire Articles Crash Course in Machine Learning Supervised Learning Deep Learning GPT-3 Deepfake Photo Generation Algorithmic Detection Summary Chapter 3: Deepfake Deception Sounding the Alarm A Brief Tour of Shallowfakes The Origin of Deepfakes Some Technical Details Different Types of Deepfakes Deepfakes in Politics Detecting Deepfakes Legal Regulation Dismissing Valid Evidence Summary Chapter 4: Autoplay the Autocrats Growing Chorus of Concern Background on YouTube Development of the Algorithm 2012: From Views to Watch Time 2015: Redesigned with Deep Learning 2018: Deep Reinforcement Learning And... YouTube in Brazil YouTube’s Political Influence on Brazilians How the Far-Right Was Favored Conspiracy Theories Flourished Playing the Game YouTube in America Stirring Up Electoral Trouble in 2020 YouTube in the American Media Landscape Studying the Algorithm Pew’s Random Walk Chaslot’s Political Recommendation Data Tracking Commenters Contradictory Results Longitudinal Study The Role of Viewing History Another Algorithmic Misfire Moderating Content on YouTube Summary and Concluding Thoughts Chapter 5: Prevarication and the Polygraph History of the Polygraph Marston and His Invention Into the Courtroom From Blood Pressure to Polygraph Determined to Find a Use Enduring Skepticism The Polygraph Meets AI Lies in the Eyes Deep Learning Micro-Gestures From Video to Audio and Text Fake News Summary Chapter 6: Gravitating to Google Setting the Stage Google Maps Fake Business Information Google Images Google Autocomplete Suggesting Hate Suggesting Fake News Google News Google Search Ranking Matters Signals the Algorithm Uses Featured Snippets Blocking Search Results BERT Elevating Quality Journalism Summary Chapter 7: Avarice of Advertising Google Ads and Fake News 2017 Report 2019 Report 2021 Report Racism in Google Advertising Facebook Ads and Racism Offensive Ad Categories Racist Exclusionary Advertising Algorithms Could Help Instead of Hurt Illegal Exclusionary Advertising Continued Legal Action Algorithmic Bias Corporate Progress What of Fake News? Concluding Thoughts Summary Chapter 8: Social Spread Setting the Stage Those Who Rely Primarily on Social Media Social Media Algorithms Facebook’s Problems and Reactions Why QAnon Is So Tricky Quantifying the Spread of Fake News Twitter and the 2016 Election Lies Spread Faster and Deeper Than Truth The 2020 Election How Algorithms Have Helped Facebook’s Machine Learning Moderation Twitter’s Bot Detection The 2020 Election and Its Aftermath What Else? How Algorithms Could Help Structural Approaches Fake News Detection Algorithmic Adjustments Section 230 Concluding Thoughts Summary Chapter 9: Tools for Truth Online Tools Full Fact Logically Squash FakerFact Waterloo’s Stance Detection SciFact Diffbot Twitter Bot Detection Google Reverse Image Search Additional Tools Fact-Checking on the Big Platforms Google YouTube Facebook Twitter Summary Index