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Everybody lies, to friends, lovers, doctors, pollsters – and to themselves. In Internet searches, however, people confess their secrets – about sexless marriages, mental health problems, even racist views. Seth Stephens-Davidowitz, an economist and former Google data scientist, shows that this could just be the most important dataset ever collected.This huge database of secrets – unprecedented in human history – offers astonishing, even revolutionary, insights into humankind. Anxiety, for instance, does not increase after a terrorist attack. Crime levels drop when a violent film is released. And racist searches are no higher in Republican areas than in Democrat ones. Stephens-Davidowitz reveals information we can use to change our culture, and the questions we're afraid to ask that might be essential to our health – both emotional and physical. Insightful, funny, and always surprising, Everybody Lies exposes the biases and secrets embedded deeply within us, at a time when things are harder to predict than ever.

Data mining --- Big data --- Internet --- Social aspects --- change cultuur economie fintech gedrag innovatie robot strategie technologie verandering --- Internet - Moral and ethical aspects. --- Internet - Social aspects. --- Internet users - Psychology. --- Social aspects. --- Philosophy and psychology of culture --- Computer architecture. Operating systems --- Sociology of culture --- Mass communications --- Information systems --- Social psychology --- PXL-Research 2018 --- internet --- Big Data --- maatschappijkritiek --- Data mining - Social aspects --- Big data - Social aspects --- Internet - Social aspects

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Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our world—provided we ask the right questions. By the end of an average day in the early twenty-first century, human beings searching the internet will amass eight trillion gigabytes of data. This staggering amount of information—unprecedented in history—can tell us a great deal about who we are—the fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable. Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women? Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential—revealing biases deeply embedded within us, information we can use to change our culture, and the questions we’re afraid to ask that might be essential to our health—both emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. Everybody Lies challenges us to think differently about how we see it and the world.

Données massives --- Identité numérique. --- Protection de l'information (informatique) --- Traces numériques. --- Aspect social.

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Internet --- Data mining --- Big data --- Social aspects. --- Social aspects. --- Social aspects.

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Big data.