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664 reviews for:
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us about Who We Really Are
Seth Stephens-Davidowitz
3.82 AVERAGE
664 reviews for:
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us about Who We Really Are
Seth Stephens-Davidowitz
3.82 AVERAGE
Great good on how to approach big data and the potential advantages and dangers of using big data for decision making.
I chose this book as part of Book Riot's 2018 Read Harder challenge for a book about social science. I really wanted to give this book 2 stars, maybe 2.5 stars because I found myself really just wanting to get through it. I'm not sure if that is 100% the book's fault or partly mine because I wanted to move on. Either way, here we are.
In general, I did not care for this book. Mainly, the structure, writing style, and immense biased and arrogance that is dripping from this book. First, this book felt like chaos. I'd be reading a paragraph about his thoughts on what "Big Data" says about raising your kid in a certain area and the next sentence would dive straight in to a study he did on what people googled about pregnancy.
There were some very interesting "facts" in here, that really should be called "insights" maybe. There were a lot of things that really any one can draw from Google Trends but the author makes very bold proclamations from it. Other times, there were very interesting facts backed by other studies and science.
Maybe this just wasn't for me. I've read a few books about social science but this one just didn't sit well. It felt like stream of consciousness a lot of times with some graphs throughout, with a lot of sports stories and sex. Either way, lots of people loved it so it may be 30% me and 70% this book.
In general, I did not care for this book. Mainly, the structure, writing style, and immense biased and arrogance that is dripping from this book. First, this book felt like chaos. I'd be reading a paragraph about his thoughts on what "Big Data" says about raising your kid in a certain area and the next sentence would dive straight in to a study he did on what people googled about pregnancy.
There were some very interesting "facts" in here, that really should be called "insights" maybe. There were a lot of things that really any one can draw from Google Trends but the author makes very bold proclamations from it. Other times, there were very interesting facts backed by other studies and science.
Maybe this just wasn't for me. I've read a few books about social science but this one just didn't sit well. It felt like stream of consciousness a lot of times with some graphs throughout, with a lot of sports stories and sex. Either way, lots of people loved it so it may be 30% me and 70% this book.
2020 EXHORTATION Wednesday, 29 July 2020, the four horse-manuremen of the datapocalypse will testify before Congress about their insane, untrammeled greed and its deleterious effect on Society. (I am presupposing the end result of the hearing here because I am under no obligation to hide my own opinion of these nauseating monopolists.)
2019 EXHORTATION We're entering the 2020 election cycle for real at this moment. Please, all US citizens, PLEASE read books! Especially books about data, how it's acquired and analyzed, how it's massaged and manipulated—the more you know about the topic, the harder it will be for agenda-having politicians to lie to you with numbers.
I have nothing unique to add to the conversation about this book. I think those most in need of reading it won't, and that's frustrating.
If you've ever seen a number adduced to explain a trend, read this book. If you've ever asserted that a certain percentage of something was something/something else, read this book. If you've ever seen a politician quote a study and your innate bullshit filter clogged up, read this book.
Really simple, high-level terms: READ. THIS. BOOK.
2019 EXHORTATION We're entering the 2020 election cycle for real at this moment. Please, all US citizens, PLEASE read books! Especially books about data, how it's acquired and analyzed, how it's massaged and manipulated—the more you know about the topic, the harder it will be for agenda-having politicians to lie to you with numbers.
I have nothing unique to add to the conversation about this book. I think those most in need of reading it won't, and that's frustrating.
If you've ever seen a number adduced to explain a trend, read this book. If you've ever asserted that a certain percentage of something was something/something else, read this book. If you've ever seen a politician quote a study and your innate bullshit filter clogged up, read this book.
Really simple, high-level terms: READ. THIS. BOOK.
This book is just okay. It has some interesting facts and ideas but there is a lot of repetitive commentary which really isn’t all the interesting, he’s just not that great a writer. While the idea of analyzing google searches is interesting and novel he hypes it a lot at the expense of previous methods. He seems to think that people are more honest in google searches and even in a few instances gives complementary information to support this, but overall were expected to just accept it’s “digital truth serum.” I found some of his reasoning to be particularly weak, for example when trying to explain that when people search for “nigger” they aren’t searching for rap lyrics because rap lyrics use “nigga”. While that may be true, he’s conflating the search word with the actual word in the lyrics. I mean, how do we know people know to search for “nigga” when looking for rap lyrics? I came away wondering whether I could trust his results and if I really learned anything.
Entertaining story about what you see when you take a curious look and combine Google search data, other big data and ask the right questions. The book is highly recommended for all people interested in data science.
An enjoyable, surprisingly heartfelt, romp through the world of Big Data and what it reveals about our society and ourselves.
I was hoping for more hard science and statistics in this book – it did provide a large number of numerical facts, but the conclusions seemed a bit rushed and unconvincing at some points. Furthermore, I couldn't shake the feeling that the book was actually written in 2013-ish when Big Data and A/B Testing were all the rage and were going to change *everything* – in 2017 it somehow manages to feel already dated. Big data is no longer the prime buzzword and we already know that excessive A/B testing leads to a number of other problems (like fake news, for example).