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Summary, Analysis, and Review of Seth Stephens-Davidowitz's 'Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are'
- Gesprochen von: Michael Gilboe
- Spieldauer: 27 Min.
- Ungekürztes Hörbuch
Please Note: This is a key takeaways and analysis of the book and not the original book.Start Publishing Notes' Summary, Analysis, and Review of Summary, Analysis, and Review of Seth Stephens-Davidowitz's Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are includes a summary of the book, review, analysis and key takeaways, and detailed "About the Author" section.
In Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are, Seth Stephens-Davidowitz cleverly manipulates Big Data to explore the dark underside of American consciousness. Using powerful new methodologies, the author analyzes anonymous Google searches across a wide range of subjects about which people tend to be secretive. From Internet pornography to medical diagnoses, Stephens-Davidowitz reveals startling truths about people's desires, failures and fears. Published in 2017, the book begins with a memory that's fresh for listeners: the unexpected ascension of Donald Trump to the US presidency. While pundits, including data journalists like Nate Silver, didn't see it coming, the clues were planted in Big Data. Stephens-Davidowitz, who has been working with giant data sets of search-related data on sites like Facebook, Stormfront, Wikipedia, PornHub, and especially Google over the last decade or so, saw some intimation in the "tea leaves". Before the 2016 election, he had already uncovered troubling data about how flagrant racism had negatively impacted Obama's elections, though of course the results were in the politician's favor. Stephens-Davidowitz's work in this area was rejected by a number of the journals at the time because so many pundits believed in a post-racial America, but his findings anticipated the white nationalism that propelled Trump into office.