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Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

Seth Stephens-Davidowitz (Goodreads Author),

Steven Pinker (Goodreads Author) (Foreword),

شایان سادات (Translator )

3.90 · Rating details · 36,674 ratings · 3,349 reviews

Foreword by Steven Pinker

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 Foreword by Steven Pinker

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. ...more

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Hardcover, 338 pages

Published May 9th 2017 by Dey Street Books

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Apr 26, 2017 Jessi rated it it was ok

This book tries too hard to be Freakonomics. The first two parts are full of random examples of interesting but mostly pointless things that can learned via Google search trends. However, a whole lot of assumptions are made off these bits of data that don't seem to have much basis in factual scientific methods of research. Unprofessional jokes are thrown in randomly. If you need a footnote to explain why a joke was not homophobic maybe you should have just skipped the joke. And any book of less ...more

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May 02, 2017 Will Byrnes rated it really liked it

Shelves: nonfiction, brain-candy, computers-ai

…people’s search for information is, in itself, information. When and where they search for facts, quotes, jokes, places, persons, things, or help, it turns out, can tell us a lot more about what they really think, really desire, really fear, and really do than anyone might have guessed. This is especially true since people sometimes don’t so much query Google as confide in it: “I hate my boss.” “I am drunk.” “My dad hit me.”

There’s lies, damned lies and then there are statistics. One must w ...more

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Oct 23, 2017 Lori rated it it was amazing · review of another edition

When sociologist ask people if they waste food, people give the only correct answer. It's wrong to waste food.

When sociologist survey the contents of the same people's garbage, they get a more accurate answer.

Just imagine how much more information is available trolling through internet searches.
...more

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Feb 21, 2018 Richard Derus rated it really liked it

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 ...more

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Jan 20, 2018 David Rubenstein rated it really liked it

Shelves: business, computers, audiobook

This is an engaging book about how big data can be used to improve our understanding of human behavior, thinking, emotions, and preference. The basic idea is that if you ask people about their behavior or their preferences in surveys, even anonymous surveys, they will often lie. People do not like to admit to low-brow preferences; racists do not want to admit to their prejudices, most people who watch pornography do not want to admit to it, and even voting is often misrepresented; some people wh This is an engaging book about how big data can be used to improve our understanding of human behavior, thinking, emotions, and preference. The basic idea is that if you ask people about their behavior or their preferences in surveys, even anonymous surveys, they will often lie. People do not like to admit to low-brow preferences; racists do not want to admit to their prejudices, most people who watch pornography do not want to admit to it, and even voting is often misrepresented; some people who voted for Trump would not admit to it.

But, by analyzing immense datasets from Google, public archives, social media, and the like, Seth Stephens-Davidowitz has been able to unearth a lot of fascinating answers to puzzling questions. For example, he is able to predict, through Google searches for various symptoms, who is likely to have early stages of pancreatic cancer. He can predict epidemic breakouts of some contagious diseases well before they are announced by the CDC (Center for Disease Control). He shows that the single factor that correlates with voting for Trump is that of racism.

Then there are the fun factoids, about the sorts of things that people search for most often on Google. Most commonly, the search "Is my son ..." is followed by "gifted", while the search "Is my daughter ..." is followed by "overweight". That tells us something about stereotypes for the way people think about their children. Interestingly, the release of a new violent movie in a city is correlated with a decrease in violent crime in that city. Perhaps the reason is that violent people who are watching the movie are not out on the streets, committing crimes.

And here we get to the main problem with this sort of analysis. Undoubtedly, the research and analysis of big datasets is done correctly. However, once a surprising result is found, understanding the motivations behind the online activity are often subjective and open to interpretation. While this book is very careful about its underlying assumptions, it is a slippery road to getting the correct interpretations and explanations.

This is an easy, well-paced book that should appeal to anybody who enjoys books like Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. ...more

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Dec 30, 2019 Eli ad rated it it was amazing

such an interesting book, it broaden my views, i'm looking forward to read more books of the author ...more

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Jun 09, 2019 Monica rated it really liked it · review of another edition

Shelves: science, soc-sci-politics, science-statistics-math-data, science-technology, pub_2010s, science-ai-computer, kindle_nonfiction, behavior-psychology

Everybody Lies has all the makings of the kind of book I get suckered into buying during an amazon kindle sale. A pop culture polemic that has a very short half-life of relevancy. After reading it, my first blush was to say that I was spot on. But as I thought about it, I realized it had more depth. That's likely because Seth Stephens-Davidowitz is an actual scientist trying to educate people about what they are actually revealing with everything that they say and do.

The late 20th Century has h Everybody Lies has all the makings of the kind of book I get suckered into buying during an amazon kindle sale. A pop culture polemic that has a very short half-life of relevancy. After reading it, my first blush was to say that I was spot on. But as I thought about it, I realized it had more depth. That's likely because Seth Stephens-Davidowitz is an actual scientist trying to educate people about what they are actually revealing with everything that they say and do.

The late 20th Century has heralded access to vast quantities of information on every one of us. Our buying habits, browsing habits, what news sources that we use in a very proliferated world of news access. We are telling about ourselves every time we go online on our tablets, phones and computers. Every text, every phone call, every e-mail is adding data to our digital make up. Like it or not, data is collected and available for each and every one of us on some very personal things. There is a field of science dedicated to analyzing the data and interpreting its meaning. The byproducts of this new field are used for good and evil. Corporations can use the information to target people who may buy their items (How did goodreads know that I was thinking about buying a mattress?). Some data mining results could be used to determine how much people need certain types of government services. Some internet searches combined with buying habits, forum discussions, book reviews, blog posts etc have led to medical discoveries. The amount of data is staggering and the ability to compile and analyze the data to reveal useful information is a new science that goes way beyond statistics. It requires knowledge of math, and sociology and psychology and engineering and biological science and an understanding of human nature etc to attempt to mine useful information. What Stephens-Davidowitz has discovered is that everybody lies about…well everything. His primary discussion is that people rarely tell the truth in poll and surveys etc. They also lie on their online data habits. Oftentimes to themselves. That little fact obviously complicates the mining of data for example in Red States w/ their stated evangelical postures that consume the most porn and have the highest rates of internet searches for access to abortions etc. People lie in their own searches as they seek to reinforce their own positions and don't necessarily search for answers. Those kinds of actions are not surprising but they complicate analysis (understatement).

This book was very interesting data primer. There is so much more to the amount of data most of us generate every day and Stephens-Davidowitz does a great job of explaining the basics. Some of his examples and his approach are a bit superficial, juvenile, pop culture. I don't find myself curious about the users of pornhub, or average penis size or baseball stats. Some of that was silly and salacious; betraying his youth and blatantly catering to what his data mining perceived would be an audience of young males. Bah. Also, he quoted Malcolm Gladwell as a resource which in my view should never be used if you hope to build a foundation based upon experience in the field and credibility on the subject…of anything. Nonetheless, I enjoyed the book and I think Stephens-Davidowitz has a very compelling and prosperous future as both a scientist and a writer.

4 Stars

Read on kindle ...more

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Oct 25, 2019 Carole rated it really liked it · review of another edition

Everybody Lies: Big Data, New Data, and What the Internet Reveals About Who We Really Are by Seth Stephens-Davidowitz takes us into the world of social sciences via the internet. I might have found the book version a bit on the boring side but I enjoyed the audiobook. Big Data can answer any and all of our questions. But will the answer be what we want to hear. And do we need to know about all aspects of the world we live in. The book is similar to some of Malcolm Gladwell's work but it is not M ...more

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Jun 28, 2017 Amos rated it did not like it

No practicing analyst or social scientist will find anything of value in this book. It verges on being dangerously deceptive, filled with logical fallacies and half baked reasoning for it's conclusions. The book claims to be finding truth in an uncertain world, but actually is just adding to the noise. ...more

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Mar 01, 2018 aPriL does feral sometimes rated it liked it

Shelves: non-fiction, science, academic-notations

I was annoyed by the author’s writing style in ‘Everybody Lies’. I have no doubts author Seth Stephens-Davidowitz was trying to write to a large general audience, including that assumed class of American non-science reader who hates math and binge watches ‘Keeping Up with the Kardashians’. Good for him, and maybe you, right? But I became more and more annoyed as I read. Ah, well. It is an interesting and informative read, in spite of trying too hard to be fun, imho.

What is the book about? I am g I was annoyed by the author’s writing style in ‘Everybody Lies’. I have no doubts author Seth Stephens-Davidowitz was trying to write to a large general audience, including that assumed class of American non-science reader who hates math and binge watches ‘Keeping Up with the Kardashians’. Good for him, and maybe you, right? But I became more and more annoyed as I read. Ah, well. It is an interesting and informative read, in spite of trying too hard to be fun, imho.

What is the book about? I am glad to report it has genuine information about the science of statistics and ‘big data’ collecting, and how the erroneous selection of study parameters or assumptions about what is relevant data to study affects conclusions (as far as I know - I am a dunce at scientific math, despite that I passed a statistics class). The author used what seemed to me genuinely interesting new methods to formulate statistical studies, primarily using Google’s forensic tools, along with other sources.

I was shocked by what people type into Google Search (which Google compiles into anonymous data). For example, President Obama’s race appears to have truly ignited racists into coming out of their closets. Comparing survey interviews with people who state they are racist (a low percentage) with the percentage of those who Googled “n***** jokes” state by state turns out to show some truly hidden pockets of unexpected racism - and the total percentage of racist searches on Google was WAY higher than the racism that typical surveys show. In addition, those places who adore Trump also searched most for “n***** jokes”. Correlation? Idk, no one does know for the record, but I think yes.

Also of interest to me (please don’t bust my balls because of my prurient interests - and maybe there is a pun in this sentence, hehheh - read on) men really truly do Google a lot about penis sizes. Come on, fellas, give it a rest! (Yes, I am trying to be snarky since the too much ‘at rest’ position is part of what men appear to be most anxious about!) Men prowl porn sites in humongous numbers - shocking, right? - which is good for statisticians looking for Truth about sexuality for their inputs into their mathematical equations. Based on Google porn searches, the author estimates 5% of the population is gay. (Btw, conservatives mostly use the word ‘homosexual’ while liberals use the phrase ‘same-sex’, statistically, in Google searches.)

Not to neglect what Google says about what the ladies’ biggest sexual worry is, all I can say is, Oh. My. God. Vagina odor. Really? Really!!

All statisticians should take note - interrogative surveys often show different results from those statistics revealed in Google searches about the percentages of who is thinking/feeling what where and when, especially in those morally-weighted or personally embarrassing areas of society. Of course, interpretation is always fraught with possible erroneous judgements whatever the source of sampling.

I have always trusted those insurance actuarial tables FAR more than political or media spins or even university data studies - so now I am adding Google statistics to my ‘trusted info’ list. Of course, gentle reader, I know any compilations of data can be erroneously or purposely manipulated or massaged. ‘Garbage in, garbage out’ still applies...which is the case ‘Everybody Lies’ makes as well. The book seemed on top of the science, as far as I know. I am not a science-brain, but an amateur wannabe.

My one irritation with this book is all about the manner in which the information is explained. Gentle reader, my complaint is subjective as hell. Honestly, I can’t put my finger on it, though. The writer seemed to be trying to fill out his actual 200-page book to 300 pages by having personal emotional filler similar to the gaspy asides many shows use to increase the viewers’ emotional high about what is being discussed. Are you familiar with those TV shows that, after each commercial break, recap the entire show in the preceding minutes before the commercial break in a breathless montage manner? And they often had a shocked-gasp teaser of what will be shown before the commercial break? Anyway, I felt there was a lot of that style of emotional manipulation (and extending of the material) going on in this book, somehow. I simply did not appreciate the personal ‘fun’ filler so much. Maybe there wasn’t enough snark. I prefer snarky humor, if there is humor. Bite me. Maybe a more tightly edited book would have worked better for me to enjoy reading it. Anyway, I realize I am floundering about here. None of this may be true at all for you.

Ultimately, this is a book worthy of reading for the general reader (for the record, I definitely have a lit/history brain, so yes, I am a general science reader!) and the explanatory information about how statistical studies are done (the only math-involved college class which engaged me) and what people are really feeling and thinking (if Google searches are to be believed, and I think they are).

Included are extensive Notes and Index sections. ...more

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Nov 27, 2017 Rachel rated it did not like it

I wanted to like this book. It's an interesting topic. But I found the methodology extremely sloppy. Or maybe the author just omitted some key facts. He was clearly determined to prove that racism caused the election of Donald Trump. But it's disconcerting to read the conclusion BEFORE the data analysis itself. On one hand, he says that Obama easily won two terms, DESPITE racism. Then he quickly says that Trump won the 2016 election BECAUSE of racism. So which is it? Is racism so widespread that I wanted to like this book. It's an interesting topic. But I found the methodology extremely sloppy. Or maybe the author just omitted some key facts. He was clearly determined to prove that racism caused the election of Donald Trump. But it's disconcerting to read the conclusion BEFORE the data analysis itself. On one hand, he says that Obama easily won two terms, DESPITE racism. Then he quickly says that Trump won the 2016 election BECAUSE of racism. So which is it? Is racism so widespread that it caused both candidates to win, the black man despite it, the white man because of it? It makes no sense. Nor was I terribly convinced that Google searches for the word n-gger are actually clearcut reflections of a person who would never vote for a black president but always vote for Trump. That's a pretty big leap of logic. As is his notion that black people would spell it "n-gga" therefore all these searches are by white racists. Also, is it a real absolute that racists would never vote for a black president? After all, you could vote for Obama because you feel he's the best of two options, yet still be a flaming racist. Likewise, if you search Google for racist jokes, does that actually prove that you are treating minorities unfairly? It may sound like a reasonable conjecture but this is data science, not an op-ed column. There should be a more decisive connection before making a grand sweeping statement that Trump won due to racists but Obama won despite racists.

The author is even sloppier in the section on searches of a pornographic nature. He refers to a data set from a porn site called PornHub. He has to assume that anyone who registers on that site and states "I am male" or "I am female" is absolutely telling the truth. But how do we know that? Are we sure that men never pretend to be women to chat with others, exchange messages, or share videos on porn sites? I'm not convinced.

As was widely reported, 25% of searches by (alleged) women on porn sites are for rather violent porn. I don't mean a little spanking, but hardcore search terms including words like "brutal" and "crying" and so forth. 20% of the (alleged) women's searches are for lesbian porn. But the author is quick to point out: this is sexual fantasy! It's not real life! Those women aren't actual lesbians, nor do they want to engage in violent sex.

But when it comes to men's searches, he regards those as literal fact. If men search for gay porn, it's because they're gay, maybe closeted, but definitely gay. Why does he insist this is true for men, but not for women?

The same sloppy reasoning is applied to various other search terms. The fact that "boyfriend won't have sex" is far more common than "girlfriend won't have sex" is the foundation for his notion that men are more likely to refuse sex to their partners than vice versa. But how do we know that's actually true? What about the notion that women are more likely to SEARCH for a solution to this problem online?

The fact is that we know absolutely nothing about the people performing these searches - whether they are male or female, racist or fair-minded, gay or straight. So making assumptions about their motivations based solely on search terms is just poor data science. Maybe there's some essential research that the author omitted. But it looks like pure speculation based on search terms, which is not what I would expect of an author who claims to be a data scientist.

Stick with Freakonomics if this topic interests you. ...more

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Apr 17, 2018 Jim rated it it was amazing · review of another edition

Shelves: 1audio, 2non-fiction, science

I am now convinced that Google searches are the most important data set ever collected on the human psyche. writes the author early on & he shows why. (Google trends is available to all here: https://trends.google.com/trends/) He also checked other big data sets including Wikipedia, Facebook, Pornhub, & even Stormfront, the largest racist site. What he found was really interesting & it will help harden the soft, social sciences. It's a new frontier.

He points out problems with traditional report I am now convinced that Google searches are the most important data set ever collected on the human psyche. writes the author early on & he shows why. (Google trends is available to all here: https://trends.google.com/trends/) He also checked other big data sets including Wikipedia, Facebook, Pornhub, & even Stormfront, the largest racist site. What he found was really interesting & it will help harden the soft, social sciences. It's a new frontier.

He points out problems with traditional reporting. In the section about child abuse & abortions, Google searches suggest that child abuse does increase during economic downturns while gov't figures incorrectly show little change. Closing abortion clinics doesn't stop them, it simply leads to more self-induced abortions. Both happen off the books, but there is now convincing supporting data to show us what we need to address & make more informed decisions with resources.

Big data has an advantage over every other type of survey because few realize it is being collected, so we don't lie to make ourselves look better. It's also anonymous & aggregate, so caution needs to be used when forming conclusions. For instance, based on Pornhub searches, the author concludes that about 5% of men are gay because they searched for gay porn. That seemed a reasonable conclusion until he pointed out that 15% of women search for rape porn. Does that mean they want to be raped? The author says of course not & makes a big deal out of the difference between fantasy & reality. That makes me question his first conclusion, although it seems about right.

Gut reactions are often wrong & he provides several examples where it's wrong due to cognitive biases. He also points out "The Curse of Dimensionality". Given large enough sets of data, there will be correlations just through chance. For instance, there are graphs that show how closely autism diagnoses track with organic food sales or Jenny McCarthy's popularity. Separating these out is a whole other problem.

Big Data only gives us trends that we need to examine. We can't use it on the individual level. While 1000 people searched for how to kill their girl friend, only 1 girl was killed in his example. That's horrific & might have been stopped if someone had looked at his search history, but do we give up everyone's privacy for a 1 in 1000 chance that we might prevent a murder? Some might be willing, but I'm not, so we also have new questions to address.

The audio book was well narrated & I didn't miss the graphs too much. They're provided in the extra material, but weren't handy when I was listening & the book took that into account for the most part. Highly recommended in either format. ...more

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Nov 07, 2017 Trish rated it liked it

Recommended to Trish by: Lori

Shelves: audio, political-science, science, economics, sexuality, computerology, social-science, nonfiction, psychology, math

Maybe everyone does lie. But they don’t lie all the time. Stephens-Davidowitz makes the good point that asking people directly doesn’t always, in fact may not often, yield true answers. People have their own reasons for answering pollsters untruthfully, but it is clear that this is a documented fact. People sometimes lie to pollsters.

Stephens-Davidowitz was told by mentors and advisors not to consider Google searches worthwhile data, but the more he looked at it, the more he was convinced that G Maybe everyone does lie. But they don’t lie all the time. Stephens-Davidowitz makes the good point that asking people directly doesn’t always, in fact may not often, yield true answers. People have their own reasons for answering pollsters untruthfully, but it is clear that this is a documented fact. People sometimes lie to pollsters.

Stephens-Davidowitz was told by mentors and advisors not to consider Google searches worthwhile data, but the more he looked at it, the more he was convinced that Google searches contained the best data for determining what people are concerned about. He has uncovered some interesting trends that are not apparent through direct questioning because people are sometimes ashamed of their fears, feelings, prejudices, and predilections.
♾
I didn’t really like this book. Partly the reason is because I listened to it, and Stephens-Davidowitz gives charts, graphs, data points that obviously cannot be represented in the audio version. These usually help me to grasp things easily and maybe bypass pages of material that is not as interesting to me. It wasn’t that his material was hard, it was that I oftentimes did not like what he was talking about. He had a tendency to focus on deviant behavior, e.g., sexual predators, abuse, porn, etc. One might make the argument that these behaviors are important to understand and therefore worth looking at. Possibly. However, if ‘everybody lies,’ one might make the argument that we do not have to look at deviance to find untruthfulness.

What we discover is that to test Stephens-Davidowitz’s thesis that ‘everybody lies,’ we have to spend quite a lot of time with statistics and creating studies, or as he is wont to do, studying big data. Big data probably irons out discrepancies in the reasons for our Google searches, e.g., that it is not me that is interested in the herpes virus, it is my brother, because in the end it doesn’t matter why we did the search; what matters is that we did the search. Besides, maybe I’m lying about my brother having the virus, but my interest in the topic is not a lie.

Stephens-Davidowitz has made a career so far out of the study of big data, showing us ways to slice and dice it so that it is useful to our view of the world. Only thing is, I am not as interested in what big data tells us as he is. He’d trained as an economist, and towards the end of the book he hit a couple of areas I did find more interesting, like the notion of regression discontinuity, a term used to describe a statistical tool created to measure the outcomes of people very close to some arbitrary cut-off.** S-D talks about using this tool on federal inmates, discovering criminals treated more harshly committed more crimes upon their release. But S-D also studied students on either side of the admissions cut-off for the prestigious Stuyvesant High School: those who attended Stuyvesant did not have a significant performance difference in later life than students who did not.

Apparently Stephens-Davidowitz went into data science because of Freakonomics, the bestselling book by Steven D. Levitt. He believes that many of the next generation of scientists in every field will be data scientists. I did finish the audiobook, another study he took note of in the last pages. Apparently few readers finish ‘treatises’ by economists. He believes this is his big contribution to our knowledge base, and there is no doubt his contrariness did highlight ways big data can be used effectively.

If I may be so bold, I might be able to suggest a reason why many female readers may not be as interested in the material presented, or in Stephens-Davidowitz himself (he was/is apparently looking for a girlfriend). Stay away from the deviant sex stuff, Seth. It may interest you but I can guarantee that fewer women are going to find that appealing or reassuring conversation or reading material.

An interesting corollary to this economists’ data view is the question of whether the truth matters, which is how I came to pick up this book. Recently on PBS’ The Third Rail with Ozy, Carlos Watson asked whether the truth matters. At first blush the answer seems obvious, and two sides debated this question. One side said of course truth matters…but most of us know one man’s truth to be another man’s lie. The other side said ‘everybody lies.’ It got me to thinking…I do think the two ways of coming to the notion of lying dovetail at some point, and one has to conclude that truth may not matter as much as we think. What matters is what we believe to be true.

Finally, it appears Stephens-Davidson agrees to some degree with Cathy O'Neill, author of Weapons of Math Destruction, in that he agrees you best not let algorithms run without human tweaking and interference. The best outcomes are delivered when humans apply their particular observations and knowledge and expertise along with big data.

** S-D describes it this way:

“Any time there is precise number that divides people into two different groups, a discontinuity, economists can compare, or regress, the outcomes of people very very close to the cut off.”

...more

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Jun 05, 2017 Matt Ward rated it it was ok

This book could have used a good editor. It tries to be a Gladwell-type of book without fully succeeding. Issue 1 is that the anecdotal stories are not fleshed out enough to really draw you in like Gladwell does. This causes much of the book to come across as a list of facts, and it gets pretty old by the midway point.

The other issue is a growing trend among people writing data books. They want to write in a colloquial style to make it seem informal and easy to read. They don't want to scare off ...more

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Nov 20, 2017 Yaaresse rated it it was ok · review of another edition

Shelves: non-fiction, computers-and-tech, social-sciences, 21st-century-setting, first-read-2017

At 58%, I give up. DNF.
I've seldom read anything that contained so many individually interesting (if shallow) sentences and still bored the hell out of me. I'm also tired of reading about the author's infatuations with baseball, Google, and porn. I am counting this book as read, however, because I should get some small (if valueless) reward for the time I lost reading it.

Some random, non-linear thoughts because I'm not interested enough in the book to try harder at this point:
1. The author wor At 58%, I give up. DNF.
I've seldom read anything that contained so many individually interesting (if shallow) sentences and still bored the hell out of me. I'm also tired of reading about the author's infatuations with baseball, Google, and porn. I am counting this book as read, however, because I should get some small (if valueless) reward for the time I lost reading it.

Some random, non-linear thoughts because I'm not interested enough in the book to try harder at this point:
1. The author worked for Google. Apparently, he's still smitten by them and thinks their massive collection of data on users is the most wondrous gift to humans ever. Of course he does. His degree and career depend on it.
I am of the opinion that the most, and perhaps the only, honest thing Google has ever done is when they got rid of their "Don't be evil" corporate slogan. Google is kind of like Walmart to me. I don't like it, trust it, or believe anything it claims and use it as little as possible.
2. The fact that the government turned over every American's tax data to whatever researchers want to dig through it--and in such detail that they can trace income changes for every address a single individual has reported from--is worrisome. "Oh, but the individuals weren't identified." Bullshit. Maybe not by name, but there is enough info there to cross-match with other data and individually identify people. That every search you've every made is time/location stamped and available to whoever wants it is just effing creepy. While we all should know by now that we're just products to be data-mined for profit, seeing a time-stamped list of an individual's exact searches over a 24-hour period is disconcerting.
3. If this guy is right, I've been using search engines for all the wrong things. (Read that in snark font.) Do people really do Google searches for "Why are Jews cheap?" or "Is my daughter ugly?" or "Am I gay?" Gee, all this time, I've been using searches for things like "What is the capital of Latvia" or "What is the formula to calculating amortization?" Every now and then I sink to searching for David Bowie music videos. I think I once stooped to "Why the hell are the Kardashians famous?" Supposedly women everywhere are Googling "Does my vagina smell?" and "Is my husband cheating"? Well, honey, if you have to ask....
4. Data for data sake serves little purpose. It just makes for more noise, not more clarity.
5 . Apparently the author believes that the way to keep readers' interest is to use the most sensationalist examples he could come up with. Throw around a lot of racist terms and references to sex kinks or insecurities. When all else fails, talk baseball and throw in some frat boy humor. I get it: the porn industry drives nearly every internet innovation, from web design to security and data collection. Fine. But the tenth or 15th time he refers to women being worried about vaginal odor or how common Pornhub searches for incest videos are, it comes off as an attempt to be provocative rather than informative. For a chapter or two when that content is relevant to the topic, fine. Every single chapter? Boring. And really creepy after a while.
6. If there is any discussion about the ethics of data mining in this thing, it's so far buried in the back that I didn't get to it. If there is substantive material on statistical relevance, hypothesis testing or phantom populations, it's buried in the back.

He's the kid who walks in with his bright, shiny, non-traditional method and basically says he's right and everyone else who ever studied data is wrong.

(And maybe, if as the author claims, the last chapters of books don't get read as much as the beginning ones, it's because books are getting less and less informative and/or credible.) ...more

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Jul 15, 2017 Emma Deplores Goodreads Censorship rated it really liked it

Shelves: nonfiction, 3-stars-and-a-half, united-states, sociology, science

3.5 stars

This is an engaging and informative book about the huge amount of data available online and what it tells us about society. I read it alongside Dataclysm and found Everybody Lies to be by far the better of the two, presenting a wealth of information in a cohesive fashion and making fewer unfounded assumptions. The author was a data scientist at Google, and draws in large part on the searches people make on the site, along with information from sites including Facebook and Pornhub.

There’ 3.5 stars

This is an engaging and informative book about the huge amount of data available online and what it tells us about society. I read it alongside Dataclysm and found Everybody Lies to be by far the better of the two, presenting a wealth of information in a cohesive fashion and making fewer unfounded assumptions. The author was a data scientist at Google, and draws in large part on the searches people make on the site, along with information from sites including Facebook and Pornhub.

There’s a lot of interesting stuff in the data, from the rate of racist searches in the rust belt predicting the rise of Donald Trump, to common body anxieties and whether they actually matter to the opposite sex, to an estimate of how many men are gay and whether that varies by geography (it appears not), to rates of self-induced abortions. This is a great book to read if you love unusual factoids, whether on sexual proclivities or how sports fans are made.

The author also writes in a compelling way about the uses of Big Data itself, and while he waxes evangelical about it (evidently preferring to spend all his time immersed in statistically significant data, he finds novels and biographies too “small and unrepresentative" and therefore uninteresting), there are certainly a lot of possibilities there. In health, for instance, compiling early searches about symptoms with later searches for how to handle a diagnosis can help doctors detect pancreatic cancer at an earlier stage, while epidemics can be tracked through symptom searches. The author is also interested in how applying data can revolutionize a field, discussing at length the data that predicted the success of the racehorse American Pharaoh. (By "at length" I mean 9 pages; this is a book that moves through a broad range of topics quickly.)

Overall, the writing is engaging and the book hangs together well, being informative while mostly resisting the urge to speculate. But the author does make a couple of assumptions worth pointing out. One is that people’s Google searches are made in earnest and for personal reasons. Certainly, you might search for “depression symptoms” out of concern that you or someone you know is depressed. But you also might want to be prepared in advance to identify warning signs, or might have encountered something in the media that sparked your interest, or you might be a student writing a paper on the topic. On the other hand, if you’re intimately familiar with depression already, you’re unlikely to google the symptoms. None of this means the author’s finding a 40% difference in rates of depression symptom searches between Chicago and Hawaii isn’t relevant, but data that’s both over- and under-inclusive serves better as a starting point for research than a definitive conclusion. It's certainly not proof that better geography is twice as effective as antidepressants, as the author suggests.

The other assumption is that everybody lies: the book insists on it, based largely on the fact that typically rosy social media posts fail to reflect all those unhappy or hateful searches. Selectively sharing information doesn’t necessarily seem to me to be lying, but the author appears invested in proving the book’s title. For instance, he discusses a particular type of tax fraud: in areas where few tax professionals or people eligible for the scheme live, 2% of people who could benefit from this lie tell it, while in areas with high concentrations of both, the rate of cheating is around 30%. The author concludes that “the key isn’t determining who is honest and who is dishonest. It is determining who knows how to cheat and who doesn’t.” This bleak view of the world fails to account for the 70% who don’t cheat even in areas with high levels of knowledge; finding that significant numbers of people cheat if they know how is a far cry from finding that everyone does.

So, like the author of Dataclysm, Stephens-Davidowitz is probably a better statistician than sociologist. But if you’re interested in Big Data, or in getting a peek at the thoughts and anxieties people ask Google about because they’re not comfortable sharing with others, this is the book I recommend. You’ll certainly get a lot of interesting tidbits from it, along with perhaps new inhibitions about typing things into Google! ...more

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May 11, 2017 Tam rated it liked it

Shelves: non-fic

A pretty short book with some interesting remarks, but not yet charming enough for me. The author definitely has his quirky and funny moments, when he presents himself, his family, and especially his views more. Yet the books' ideas and findings aren't exactly ground breaking. The types of questions like in this book have been posed in Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. The usefullness of big data has been discussed by ones such as Dataclysm: Who We Are (disc ...more

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Apr 17, 2018 Cheryl struggles to catch up rated it it was amazing

Believe the hype. This is not a perfect book, but it's fun, enlightening, ground-breaking, and important. Too many people don't know the potential power of the new methodologies of data analytics, and too few ppl who think they do know that power don't know the limitations. SethSD does, and he shares a lot of what he knows with us.

This is good science for arm-chair science consumers like me, and a good read for those who just like to dabble in non-fiction. It's both concise and rich. Documented ...more

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Jun 10, 2017 Caroline rated it it was amazing

I wish I could give this book more than five stars. Anyone who has a sneaking feeling that Americans aren't who they SAY they are will find confirmation here. It's also easy to read, no academic language here.
I was already riveted by the introduction. His premise is that we all lie to each other, pollsters, and ourselves, but not to that white box where you type internet searches. Both before and after the election everyone went nuts trying to figure out why Trump was doing so much better than p I wish I could give this book more than five stars. Anyone who has a sneaking feeling that Americans aren't who they SAY they are will find confirmation here. It's also easy to read, no academic language here.
I was already riveted by the introduction. His premise is that we all lie to each other, pollsters, and ourselves, but not to that white box where you type internet searches. Both before and after the election everyone went nuts trying to figure out why Trump was doing so much better than polls would indicate, looking for factors that would explain it. There was only one. "[Nate] Silver found that the single factor that best correlated with Donald Trump's support in the Republican primaries was that measure I had discovered four years earlier. Areas that supported Trump in the largest numbers were those that made the most Google searches for 'n-----'." (He uses the real word, which deepens the revulsion you feel at what he's discovered.)
Despite Obama's two easy election victories and the narrative that we were post-racism, the Google search data tells another story about reactions to those victories.
Immediately after the San Bernadino shootings, what happened online? A ton of people searched for "kill Muslims."
And there is a lot more, about sex and child abuse and sexism. Did you know that the most common term used to complete the sentence "Is my son..." is 'gifted' or some variant thereof, and the most common term used to complete the sentence "Is my daughter..." is 'overweight'?
America is not post-anything except maybe post-good intentions.
What use does he think this can be? Well, he did have some good suggestions, and none of them are based on finding out who any individual is who's done a search. For example, if searches for "kill Muslims" spike in a certain city, a few extra police could be deployed to watch over the local mosque until the spike subsides. He spends a moment talking about how big data is not meant to be, and should not be, used to try to figure out who specifically is going to commit crimes.
By the time I was done with this book I was a bit discouraged at who Americans seem to be, but it's better to know. I hope that this kind of study continues, so we can attempt to realistically work with our society instead of pretend it's something it's not. ...more

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Jun 23, 2020 Montzalee Wittmann rated it really liked it

Everybody Lies
Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
By: Seth Stephens-Davidowitz, Steven Pinker - foreword
Narrated by: Tim Andres Pabon
Wow, this book really lays out a lot of data itself! It speaks about how people say one thing, or respond to a poll, yet they are lying. They lie less online than having to face someone.
How everything that is searched is checked and through the data searches a lot can be noted. Examples of this include what people are doing ...more

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Jul 07, 2019 Ms.pegasus rated it it was amazing · review of another edition

Shelves: nonfiction, mathematics

Author Seth Stephens-Davidowitz offers an amusing example of lying. He cites a follow-up on a survey of high school student behavior: “...a meaningful percent of teenagers tell surveys they are more than seven feet tall, weigh more than four hundred pounds, or have three children. One survey found 99 percent of students who reported having an artificial limb to academic researchers were kidding.” (Location 4702) Students like to mess with adults he cautions. Of course I didn't need to read the Author Seth Stephens-Davidowitz offers an amusing example of lying. He cites a follow-up on a survey of high school student behavior: “...a meaningful percent of teenagers tell surveys they are more than seven feet tall, weigh more than four hundred pounds, or have three children. One survey found 99 percent of students who reported having an artificial limb to academic researchers were kidding.” (Location 4702) Students like to mess with adults he cautions. Of course I didn't need to read the book to learn that. When he was a teen, my son clued me in when I expressed alarm at the results of a survey conducted by his high school indicating high usage of drugs and alcohol. What I did learn from this book was how analytical tools are helping researchers make sense of the vast amounts of data out there, data harvested from unwittingly honest interactions.

The author is a Big Data specialist and he has written an engaging and approachable book about a highly technical subject. His analytic arsenal includes Google Trends, Google Correlate, Google N-gram, and Google Adwords. He uses these tools to uncover startling and even appalling revelations about sexual proclivities conveyed in Google searches. From there he moves on to more serious topics such as the role of racism in political outcomes and gender bias themes in parenting. Most of what he reveals is unexpected and contrary to conventional wisdom in a big way.

Stephens-Davidowitz appeals to a wide range of interests in this book. He explores a concept called Doppelganger Searching as it was used to predict the trajectory of David Ortiz's career in major league baseball. (It's a perverse irony that Ortiz was the victim of mistaken identity — a figurative “doppelganger” — in a near fatal shooting).

Much has been written about abuses of Big Data. Stephens-Davidowitz is not unmindful of this problem, although his concerns will not assuage the fears of those concerned with privacy issues. If anything, the power of the tools he wields might heighten that anxiety.

Stephens-Davidowitz explains in reader-friendly fashion other technical concepts besides Doppelganger Searching. He tackles the “curse of dimensionality” — a problem relevant to social science research where the number of variables can expand almost infinitesimally. He cautions against the addictive power of metrics in another amusing example which many readers might identify with. He relates how a marketing professor became obsessed with her count on a pedometer: “She walked early in the morning, late at night, at nearly all hours of the day — twenty thousand steps in a given twenty-four hour period. She checked her pedometer hundreds of times per day, and much that remained of her human communications was with other pedometer users online, discussing strategies to improve scores.” (Location 2908)

An intriguing idea Stephens Davidowitz proposes is that Big Data analysis can breach the boundary between correlation and causation through A/B testing. He offers some tests; the reader will be astonished at how widely the data diverges from an initial gut reaction.

Explanations of behavior constantly see-saw between the microcosm of stories from individual viewpoints and confident generalizations based on flawed statistics. Seth Stephens Davidowitz sees Big Data analysis as a possible bridge between these two methodologies. This was an eye-opening book worth reading by anyone interested in public policy decision-making.

NOTES:
I read the Kindle edition of this book. The editors have taken a step in the right direction. Asterisks tag the author's comments and readers can easily toggle back and forth to the main text. On the other hand, footnotes are not marked in the text. The reader is forced to read the footnotes and conduct a search to link them back to the relevant passages. ...more

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Jun 04, 2017 Tressa rated it did not like it

Shelves: did-not-finish

I couldn't even make it through the introduction. This is a perfect example of starting with a conclusion and then finding the data to support your conclusion. All a search shows you is the number of times the word or phrase is searched. It does not show intention. It does not show the number of times a certain person searches for the same word or phrase. The author makes a lot of assumptions based on his own presuppositions. I thought this would be a fun read, but I have no intention of sloggin ...more

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Sep 21, 2017 Lubinka Dimitrova rated it it was amazing

Shelves: dark, based-on-real-events, brainworx, history-will-teach-us-nothing, thought-provoking, pop-science, psychology, are-you-serious, audio, general-non-fiction

I sought out the book after reading an interview with the author, and it was totally worth it. The book is quite enlightening, and to be honest, deeply frightening. Internet data can work miracles for the benefit of humanity, but it can bring to life many unimaginable, Big-Brother-type nightmares (current US presidents not excluded, just sayin...). Still, it's good to know. ...more

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Jul 10, 2018 Steven rated it it was ok · review of another edition

Shelves: science, non-fiction, sociology, economics, books-i-own, psychology, big-data

This book is kind of a mess, but its subject is interesting enough—and some of the findings are intriguing and potentially important enough—to make you breeze your way through it. I call it a mess, because 1) the title is Everybody Lies, yet Stephens-Davidowitz in no way shows—let alone proves—that everyone lies (only, perhaps, that people tend to keep intimate, embarrassing, and politically incorrect things to themselves or, if they do share them, are more willing to confide in Google than in o This book is kind of a mess, but its subject is interesting enough—and some of the findings are intriguing and potentially important enough—to make you breeze your way through it. I call it a mess, because 1) the title is Everybody Lies, yet Stephens-Davidowitz in no way shows—let alone proves—that everyone lies (only, perhaps, that people tend to keep intimate, embarrassing, and politically incorrect things to themselves or, if they do share them, are more willing to confide in Google than in others through traditional sources of data collection), and 2) the subtitle is What the Internet Can Tell Us About Who We Really Are, but the book is not about the Internet as such, but about the potentialities of big data in all its manifest forms (text, words, images, bio-data, and so on). Stephens-Davidowitz probably used big data to establish which words people search for and have been buzzing the most...

Anyway, having said that, the book doesn't go into nearly as much depth as I would have liked it to—assumptions go unexamined, serious ethical concerns go unaddressed, and alternative hypotheses go unexplored. However, this relative superficiality has as an upside: the book is light and entertaining, although some of the findings that S-D discusses are also shocking and depressing (like how many people search for horrible things online).

At the core of the book lie the four 'powers' of big data:

(1) It offers up new types of data (e.g., people's sexual desires and preferences gathered through porn sites).
(2) It provides honest data (e.g., people's genuine concerns as expressed through questions asked in Google).
(3) It allows us to zoom in on small subsets of people (because there is so much data, even small slices of a population can provide meaningful [statistical] information).
(4) It allows us to do many causal experiments (e.g., presumable, through the kind of A/B testing done by Facebook and sellers of ads – this is, in my eyes, a problematic area that is not adequately addressed by S-D).

For all its weaknesses, some of which merely require some more attention and research, others of which are more fundamental, Stephens-Davidowitz should get credit for the work he has done on and with big data. I do agree with him that it's a very important source of information (which ought to be used with care, all the same) and tool for research, perhaps even—as he definitely seems to think—the most important in and for the future. ...more

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Apr 14, 2019 Sonja Arlow rated it liked it · review of another edition

Shelves: recommended, non-fiction, 2019-read

3.5 stars

You may come across as liberal to the world but secretly google racist jokes…..

Although you may spill your deepest darkest secrets to Google, make no mistake this data sits somewhere ready to be analysed.

I work with big data every day, so I was immediately drawn to this book. But you really don’t need to be in the data industry to appreciate the book. It is written for the layman with humour and interesting titbits sprinkled throughout the book.

The first 3rd of the book gives a notable ...more

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Dec 05, 2017 Moshe Zioni rated it liked it

Shelves: popular-science, psychology, politics, prose, i-own, read-in-2017, audiobook, technology

Don't get me wrong, it is nice, funny and worth a short read.

A problem for me - the causality vs correlation part comes waaaay too late in the book and the author sometimes mix the two IMHO. The biggest thing to tackle for Data Scientists is the issue of causality and if/how it can be proven to be, most of the times it just cannot be proven by this method because of its built-in limitation but the author makes a pass on this and this all makes some assumptions, as possible they can be, naive in ...more

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Oct 07, 2017 Elena rated it liked it · review of another edition

The author is a bit too bragging, exaggerating, and name dropping for my taste. Still, i do not regret spending the time with the book (but would regret paying money if it would not be a library borrow).

Memorabilia. Predicting rate of unemployment with the frequency of porn site searches (amount of time on their hands). Predicting success of dating (listen, then listen some more, then, when you think you are done listening, listen some more). Doppelganger (DOPP-el-gang-er) searches in Internet ( ...more

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Aug 25, 2017 Anton rated it really liked it

Shelves: ideas, decisions-and-economics

Delightful, very engaging read on modern takes on data analysis. Fans of Levitt and Pinker I am sure will enjoy.

Hardly any 'cons' to flag up... but it is a bit on a short side and overwhelmingly US focused. Still very clever and thought-provoking

Overall: definitely worth your time ...more

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Feb 23, 2018 Wen rated it really liked it

Shelves: data-science

The title steered me a bit off-course at first—I thought it was one of those self-help psychology books that I tend to avoid.
I eventually decided to give it a shot, mostly because Steven Pinker, and author I highly respect, wrote the forward. So glad I did.
To the author Mr. Davidowitz , I did finish the book, so did I with regard to the first two books you mentioned below --moot point for the third book as it’s not even on my to-read list ;-)
“more than 90 percent of readers finished Donna Tart ...more

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Nov 25, 2017 Greg rated it liked it

Shelves: 2017, reviewed, nf-science-tech

UPDATE: In summary, the author bounces back and forth between real data/numbers and pure speculation. It's fascinating, really, as that's got to be the entire point: to show us how to tell what's real and what's fiction as we are bombarded by information..
ORIGINAL REVIEW:
Yes, "Everybody Lies" including, obviously, the author because if Seth Stephens-Davidowitz never lies, I'm sure the subtitle would have been "Except Me Within This Book". So, from our data thus far, we know the author lies, and UPDATE: In summary, the author bounces back and forth between real data/numbers and pure speculation. It's fascinating, really, as that's got to be the entire point: to show us how to tell what's real and what's fiction as we are bombarded by information..
ORIGINAL REVIEW:
Yes, "Everybody Lies" including, obviously, the author because if Seth Stephens-Davidowitz never lies, I'm sure the subtitle would have been "Except Me Within This Book". So, from our data thus far, we know the author lies, and maybe even within this book. The author's first major error comes from a hilarious statement about how gay men like Judy Garland (one can only suppose the author's sample is his gay uncles and their friends, in which case he should have used Edith Piaf instead, or maybe Bette Midler, as both Bette and Piaf were indeed placed into stardom by gay communities, while Garland was a star for everyone who went to the movies in 1939 and saw her in "Wizard of Oz". Oh, I'm digressing, sorry. I liked some of this book: specifically the parts where real numbers/data are used: for example, in 1950 a survey revealed 20% of a certain sample said they had a library card, but the official sample count indicated only 13% actually had one. (Why, oh why, would any adult in the USA NOT have a library card? This boggles my mind.) But get this ridiculous utilization of words: "...the overwhelming majority of black Americans think they suffer from prejudice....On the other hand, very few white Americans will admit to being racist." Good grief. EVERYONE has prejudices (that's how we get through this chaotic world, as we are prejudicial for, say, driving instead of flying because we like road trips and like to stop at places we have never visited and meet people we would otherwise never have a chance to speak with-and I'm talking about me, as long airport lines are no fun) but racism is a different issue entirely, as racism has nothing to do with my prejudicial decision to drive. A section devoted to "omitted-variable bias" certainly belongs in another science book this year entitled "We Have No Idea". Hilariously, the author concludes with a lot of questions, including this howler: "Where do sexual preferences come from?" The answer is simply one of genetics (in combination with epigenetics, which may turn "on" or "off" these genes) but that's old news. Hence, can we conclude that all economists and statisticians don't read current books and journals regarding genetics. Hardly, but one would assume an editor somewhere would catch this issue. In summary: when the author uses real, solid numbers (the number of likes on Facebook vs that same person's internet searches - and let's keep in mind google doesn't release names, but does release the number of loving wives who praise their husbands on facebook and then the number of wives who googles "Is My Husband Gay". Now that I think about it, why such a fixation on gay matters? A better title for this book would have been, "Everybody Lies About Their Sex Life" cause we know absolutely that is true. ...more

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Inquiry: Book Clu...: Book Club Event on 02/26/2022: Everybody Lies by Stephens-Davidowitz	1	2	Dec 03, 2021 06:34PM
Play Book Tag: Everybody Lies: Big Data, New Data	1	10	Nov 03, 2020 10:57AM
Play Book Tag: Everybody Lies - Seth Stephens-Davidowitz (4 stars)	1	9	Apr 06, 2020 05:31AM
SCPL Online NonFi...: Wrap Up	1	11	Jun 30, 2018 10:12AM
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SCPL Online NonFi...: Implicit and Explicit Bias	1	7	Jun 21, 2018 06:07PM
SCPL Online NonFi...: Data Reimagined	1	7	Jun 15, 2018 01:59PM