And What the Internet Can Tell Us About Who We Really Are

Should be required reading for anyone talking or thinking about Big Data and how it's used. This will be the first of many to make more sense of it than has been to date. It's fine to say don't start into big data efforts just for the sake of mountains of data, but have an objective, but most people haven't a clue how to begin to figure out what those objectives might be. This will help, but the future holds more ideas I'm sure. Still is nods toward the power and value of big data analyses that might give us a better world in the long run.

I read precisely 50% of "Everybody Lies: Big Data, New Data" and found it unnecessarily long winded, repetitive, and quite lacking on any big, new, or even meaningful data content for that matter. A skilled editor would have condensed the unnecessary jabber to a quarter of the book's published size. Also disappointing was that the vast majority of information presented was simply regenerated data completed by others. "Everybody Lies" is written for those green with knowledge in the area of (psydo)sociology and even then seriously lacks substance, and I assert this as a reader with an advanced degree in Sociology. Furthermore the author is strongly biased and presumptuous in his interpretations of data which he twists and falsely presents as facts. I do hope that the author continues his pursuits as a writer but in different genre such as fiction or a humorous autobiography as he does possess writing skills but most certainly he should avoid the area of non-fiction. Skip this one folks.

Very eye opening to the possibility of how ground-breaking the future of big data may prove to be. I initially heard about this book in an interview on an ESPN radio show, of all things, and I was immediately intrigued. As a person in my mid twenties with a finance/accounting background, I am very interested in data and what it tells us about the way people think and behave. However, this offered me a new viewpoint into the world of data by investigating the way people ACTUALLY think and behave by utilizing "big data" like google searches and other anonymous sources. Once I started reading, it really was hard for me to put it down. Can't wait for the next installment (hopefully). Highly recommend!

This book is a fascinating look into the world of data science. It challenges social norms and widely held beliefs and presents a case for a higher standard of honestly. Do "white lies" really guard a person's feelings? What is your wife really asking when she wonders if "this dress makes [her] look fat?" How many closeted gay men are married to women?
Understanding this book is why you should've paid attention in math class.

Who knew that Pop Tarts are a pre-disaster staple?!?! Big Data does! So many studies were profiled in this book, I totally have follow-up reading material for months! I enjoyed this book immensely and would recommend it to anyone interested in how data shows our true colors. The only reason I did not give five stars, was a repeated grammatical error.

According to the author of this book – statistically – very few people actually complete books of this type cover to cover. They usually drop out somewhere in between, but I read the whole book and was grateful for the additional insight it gave me. I keep up with the news daily (New York Times, Guardian & BBC – none of which I consider “fake news”) and read quite a lot, but was pleased to discover this fresh perspective on viewing our world. It certainly helped me make sense of our recent Presidential election and the active reemergence of racism in this country. It’s a fact that the internet is changing how our brains function and how we view everything. It only makes sense that we begin to organize and make sense of this new information. I don’t read many books of this type, but was glad I read this one and highly recommend it to others

You may have heard this is the book about the data from Google and PornHub searches. There is a chapter or two and many factoids along those lines. But the author's contention is much more than what you read in the reviews.

This book is about Big Data. Big Data which includes those searches, but also a lot more. Already years and years of books, newspapers and magazines have been digitized and made searchable. Now we can determine when the United States went from an "are" to an "is." (About 1880.) Now we can determine to a few decimal points how valuable a case of wine is based on the weather in the vineyard. If WalMart opened its treasure trove to us I suppose we could know when fungal infections peak in Texas. Just if we really needed to know.

Big Data is all those searches, all those newspapers, all the books, all the inventory data, all the telephone traffic data, all the FaceBook posts, all the sales information for everything. All of it is stored somewhere in and someday it will all be available.

Some data is Important. (We know the census is Important.) Some data is Unimportant. (Sales of steak sauce is Unimportant.) But all of it can be combined, compared and examined in ways that will be important beyond all our imaginings. This is a case of "Quantity had a Quality all its own."

The digitization of past data will change how we view our history. The oceans of data we are compiling now will allow our chroniclers to know us better than we know ourselves.

The book is somewhere between seminal and simply thought-provoking. I think you ought to read it.

One of my favorite books for 2017, perhaps my book of the year. I read Everybody Lies: Big Data.... with tons of anticipation. I enjoyed it thoroughly. I have already recommended the book to several friends and professional acquaintances. The author, Seth Stephens-Davidowitz weaves a highly readable story from a very complex, intensively detailed subject matter. The anecdotes are relevant and illuminating. The analysis is sound. This book may turn the phrase "There are Lies, damned lies, and statistics" into There are Lies, Damned lies, Statistics and Big Data Google Analysis (of truth people will tell their computer search engine). The data is right there to be analyzed & aggregated. Go buy yourself a copy and enjoy this book.

It was kind of interesting but I think the author over-estimates the value of google-searches as data. It was short though so a pretty minimal time investment and you can pick up some nice tidbits for your next cocktail party. I don't regret reading this.

This is a great book and yields a new and holistic view of what data analysis can and cannot do using Big Data. The book is clearly written and avoids jargon, and thus should be easy for non-data people to understand. It is worth the read to discover what Google Trends searches tells us about ourselves. Highly Recommend. Cannot wait for the promised follow up book. Everybody (still) Lies.

This book is a delight to read, filled with interesting facts. I found myself making a lot of noises as I read, like "hmmm,"huh!" and "hah." For those new to research, the book is a good primer on "big data," carefully outlining what analysis of big data can and cannot accomplish and where we should not tread. Stephens-Davidowitz is also careful to note the importance of looking at big data in the context of what is already known about a subject.
As a market researcher I admire Stephens-Davidowitz's ingenuity in tapping new sources of data to answer important and difficult questions. I hope people reading the book will appreciate that big data are most useful when in the hands of an inquisitive, diligent, and creative researcher and will in turn be inspired to do some of their own research.

This book is both data-rich and funny ... at the same time! The author is a PhD in economics from Harvard using cutting edge methods to study human behavior. Boy, is he finding great stuff ... and by great, I mean the stuff we need to know but would otherwise never find out. An essential and enjoyable read.

Fascinating account of what a good statistician can glean from studying Google Searches. Stephens-Davidowitz makes a good point: Facebook reveals how we'd like to appear to others, but Google Searches reveal who we really are. Interesting stuff. Well written, lively, funny and reveals more about hairless apes than is comfortable to read.

This is an interesting book with some surprising observations. I wonder, still, however, how some of the data led to the conclusions it did. It is revealing in showing people's true attitudes toward life and toward others. Overall I would recommend the book, although I question some of its conclusions.

I have audible version, well narrated. It tends to repeat a central theme, but very glad I got the book, changed my views on a number of fronts. So, recommend it, but recognize it does not get deeper and deeper on revelations.

Interesting look at how the accumulation of big data can help us understand ourselves and each other in ways that social science studies can't achieve. The revelations from the data Stephens-Davidowitz unveils left me wanting more depth and exploration.

This book is jammed packed with new tools of how to dissect the human psyche. Many of the resources and ideas could be further developed into powerful tools applicable to just about everything in life, education, health care, criminal system, marketing, to name a few. I finished the book with heightened curiosity about Big Data, and am searching for more good reads on this topic.

This is a very important book. Not because of what it claims to accomplish, but in what it represents:
BIG DATA as interpreted by ignorant, semi-educated or agenda driven politicians, businesses, journalists, bureaucrats and "social scientists" will create decisions affecting (mostly negatively) individual lives in ways we cannot possibly imagine yet.

Many critical comments point at the liberal bias of the author, which I think is one of the weaknesses of the book. But the author is so blatant about his political views, you can easily ignore them and go to the core of his conclusions - that Big Data will help turn the social sciences into a real science.

That is where the author fails completely. He does not clearly differentiate between correlation and causation. Even though the book has a chapter on that difference and the author cautions against mixing up the two, he constantly confuses them - to make a political point. This is exactly what I fear will happen with the relatively easy access to BIG DATA by practically anyone.

The author himself is a prime example. He seems so desk bound that he has no imagination of the real world - which is also likely to be a system(at)ic problem of BIG DATA interpreters. A quite laughable example is where he relates the story of a wine collector / speculator who correlates winter rainfall data from a specific French region with future prices of wine from that area. The wine lover observed that an increase in average winter rainfall correlates strongly with increases in wine price. The author then concludes that if a particular winter rainfall were a thousand centimetres higher than average, then it's worth investing an extra dollar in a bottle of wine from that area. A thousand centimetres annual rainfall occurs in a couple of places in the Lake District in England or in Assam, India, but it is a lot more than ten times the annual rainfall per year in any French wine growing region. First I thought the author was joking, but then whenever he does make a joke, he says so.
Absurd statements such as this one by the author are steps towards his ultimate conclusion in the final chapter that the analysis of big data will turn the social sciences from “pseudo” to “serious”. That is - to use Alan Sokal's book title Fashionable Nonsense only bigger. It is not the number of data points that makes a quest, inquiry or discovery scientific. As if Newton had to contemplate the proverbial falling of a million apples to derive at the laws of gravitation.
Still, of course, the eager social scientists will claim that sheer numbers will proof whatever point they are making. And there will always be ambitious politicians to use them for their purposes.
After all, poo has got to be good, a billion flies can’t be wrong.

A quick and mostly enjoyable read from a passionate user of Big Data. The next Foucault, he tells us, will be a data scientist. Guess he didn't read Foucault that carefully. Epistemological problems aside, there are great chapters on racism (more of it than we admit) and sex (less of it than we claim).

The author presents very compelling information about the research and use of big data. I would not recommend this book to anyone I know because of the references to graphic sex and offensive language brought about by researching the pornography industry. I was offended by his frequent attacks on President Trump but understanding that he's a New York Times columnist puts it in perspective.

I was excited about this book because I wanted to garner more interest in big data. I would say that the first half of the book kept me hooked. However, I slowly realized that it is incredibly difficult to make a topic such as this interesting enough in casual reading. As a textbook or an introduction to data science, I think it is one of the better books. As a book to read for fun, not so much :P

The author suggests that people don't finish books, but he admits that he was too busy getting a beer with friends to finish his conclusion.

;-)

I liked it a lot. Great read. Fascinating ideas on what we can and can't learn from big data.

Very good book with fresh insights as to what we can learn from analyzing internet behavior. Predicting future lifestyles and market demands based on Google searches is sketchy at this point, but very interesting.

The writer is passionate about learning what makes us tick and why we click. The prose are equally relatable as they are comical which made for such a smooth and easy read. I finished in only a few hours!

Complete garbage. Each chapter is nothing more than a collection of lifeless, trivial information. The book provides no insight, no depth, no nuance, and no analysis of its proposed topics. A waste of paper and a waste of time.

The first few chapters offer the best insights into the potential use of "big data" which for this author is mainly mining Google search data. However I couldn't help wondering if "less is more" for 95% of everyday life (cf. Gut Feelings by Gerd Gigerenzer)...

Author is kind of snarky, self absorbed, and preachy throughout the book. I did appreciate the chapter comparing Facebook with Google search terms. That alone is where the book's value and title are found.

click bait is usually a lot better than the content - same for this book - people's public and private faces are very different - e.g. The most frequently searched topic in the British House Of Commons is pornography & access to vast amounts of anonymous data via the internet - allows you to understand what is happening or what people are really thinking - it took a lot of words to let us know that men worry about the size of their penises and that we overstate how much sex we have but then worry privately about how much sex others are having - this is a data set of one but it is a verified purchase & accurate - wait until big data analysis has matured before buying a book about it

Seth Stephens-Daviowitz uses Google data to vilify everyone but himself and his political beliefs. This book is based on large collections of Google searches which the author manipulates in a fashion to sneakily shame and diminish people who do not share his beliefs.

This is the most irritating book I've ever read. About half way through the book I nearly threw it across the room, and would have except that I was reading it on a Kindle The author brings no thought or skepticism to any of the conclusions he draws from his observations. As many other reviewers point out, the data can be interpreted in other ways. It is only half way through the book that he discusses the distinction between association and causation. But all through the book he is drawing conclusions from what are only associations. Where I nearly threw the book across the room was when he talks about "natural" control and treatment groups. There is no such thing as "natural" control and treatment groups. These groups are carefully selected by researchers so that they know how each group differs and can control variables between groups. Then they know how to adjust the results of the study for confounding. He discusses a study by Jones and Olken where he describes countries where their leaders were nearly assassinated as the "control group" and countries where leaders were successful assassinated as the "treatment group." These are not control and treatment groups. He is just using scientific terminology to make the data sound more important than it actually is. For most of this book, the author constructs a narrative that has no basis in fact.

This book examimes data supporting the theory that everyone lies.

As a Computer Scientist, I've had my fair share of run-ins with Big Data. The book is centered on the premise that every human lies. I've found throughout my career that data can lie too, especially when manipulated by Data Scientists who openly tell you that everyone lies. If everyone lies, the only logical conclusion is that Data Scientists lie as well. So is everything in this book a lie? I guess I need to mine Big Data to get that answer.

The book starts off with a printed version of click-bait. Shock the reader. Most of what's said is oh-so obvious. Most of it is societally taboo and typically off limits for public discussion. Let see. He says people search for certain racial slurs and that he wants a true picture of racial tension in America. Those that search for a certain racial slut are de-facto racist. He explains that rap songs spell this word differently (I think he even says it twice). And therefore, according to this author, people who searched for a racial slur voted for Trump and hated Obama. It didn't take long for the author to go political on us and likely reveal his purpose for the book as a way to explain how Trump got elected (or how Hillary lost, perhaps).

Be careful, readers. Data Scientists know how to present a version of Truth using Data. Let's say I want to "prove" that people who walk on the beach live 5.6 years longer. I could claim Big Data tells us that people who Google for the keyword "beach" and "walk" are 17.1% more likely to visit a doctor. And those more likely to visit a doctor are 16.9% more likely to live 5.6 years longer. But of those, only ones living in affluent neighborhoods will actually live longer because they visit the doctor more often. The oddly named poor people (borrowing from author here) in the same town but low-income neighborhoods could be said to live longer because they live near rich people who visit doctors more regularly. Therefore people who walk on the beach clearly live longer. If a statistical problem emerges, we hide it with a massive sample size or by "zooming out" of the Big Data set a little more. If it doesn't fit our narritive, then we "zoom in" to get a closer neighborhood level view is the data. By zooming, pivoting and using Boolean connectors, we massage and manipulate the data to say or mean whatever we want. Yeah, it is a ridiculous example I just made up, but one of many simiar points the author tries to make. It turns out there is a science dedicated to Sociology which explores Big Data in deep detail to reveal the connections between all those users who are typing out racial slurs. That data isn't presented in this book, only surface level proclamations.

The author will reveal profoundities to you, such as people who visit popular porn sites might have a bad marriage and they may be (wait for it) addicted to porn. Sweeping generalizations are made about men and women and their private habits, all based on Google searches. Big Data tells us so because the Data Scientist searched for it using certain keywords.

One of the primary concerns on many peoples' minds right now is Privacy. The author makes no mention of this important facet of Big Data's usage. Our private details are merely data this type of person can mine, manipulate, and accuse us with.

I kept waiting throughout this book for the point. What is he trying to tell us? That mining Big Data is powerful and important? Duh. We've known that for years. That Big Data hides virtually unlimited hidden truths? Again, duh. That everyone is a liar? What do you think people do when the doctor asks their weight? That everyone is a liar (in one part of that spectrum or another) is a given. That no one can out-think Wall Street types? Don't agree with him on that one. The bottom line is that being predictive is hard. Especially when everyone isn't operating at face-value. Mining massive data sets is hard. Society may be on a downward spiral built from a shaky foundation of two-faced lies. Which is why Ph.d students write dissertations on it. Which is why greedmongers in lofty positions hire Data Scientists to try and get the skinny on how to trade stocks.

It is difficult and time consuming to mine these Big Datasets. And I think it is difficult for Data Scientists to resist the urge to shape and mold data in their image.

As for a conclusion, the author states he didn't write one because no one would get that far in the book (I did). What choice did I have...I kept seeking the point until the book merely ended. The main point gained from reading this is that people are secretly racist, perverted and unhappy.

So, in conclusion, this book has one or two good kernals of information. I didn't walk away with any insights or new knowledge. I expected some novel theories, some revelations about how I could go out and use Big Data for something new and innovative. Nope. For me, it didn't happen.

Wow, what can I say. Great book and a good read. He's funny talented and makes his point very straightforward. Anyone into big data, or data statistics analysis will enjoy this book.

That being said, there are a few shortfalls.
1. His reasoning is very far stretched. As a scientists he should know better than to extrapolate so much. (I guess he wanted to sell books)
2. It's extremely political. I don't find any reason why everything in this country has to be political, i'm enjoying a book, don't try to be funny and toss in petty politics.
3. How much do we need to know about Webserches for adult sites. It's almost obsessive. Yes, it's funny in the first chapters, but common man, do you really need to bring it up in almost every chapter. I tend to believe that humans are more than their adult web searches.
4. Needs to write a "control" Maybe google searches are what people feel comfortable doing in privacy. How can someone extrapolate that it's what everyone is thinking.

Seth should have done some self-reflection when rejected by the first five peer-reviewed publications where he submitted his work. Instead, he doubled down on grossly generalized conclusions. Fascinating data for sure, but they do not support his conclusions.

I won a copy of this book from Goodreads First Reads.

Wonderful book both in terms of reading and learning. Very engaging style made this a breeze to sit down and read almost in one sitting. More than that it was interesting and chockfull of examples that ran the gamut from baseball to porn to pregnancy to what makes a good conclusion in a book. It also though explains Big Data along the way - the four reasons it is can be a revolution and some more negative attributes like intrusiveness. And not just for proving I made it to the end, I thought the idea that Popper's critique of social science is undone by Big Data is a really interesting one.

Interesting premise, but a disappointing ending. The book just fizzles out at the end, with a joke about how most people don't finish books so it didn't matter. I would have been happy with this read if it would have been cheaper, as it was, I feel let down. Too short and meandering for $15

It's an axiom of the social sciences that: multiple causes often coexist, each contributing part of the explanation for the same phenomenon... A concept totally foreign to this book.

I made it 1/3 of the way through, but couldn't take it anymore. The author repeatedly takes a data-point of come kind, and then extrapolates the "why" in a way that is impossibly narrow. This book attributes a simple cause / explanation to a data-point of human behavior in a woefully reductive and misleading way.

The author might not be aware of this. He actually might believe that he has honest, solid, conclusions; but just doesn't realize the biases he's employing.

I found this book to be very informative and well researched. It definitely shows the author's political views, but that's ok as long as you take all the leftist stuff with a grain of salt.

SJWs are going to crucify this guy for pointing out some uncomfortable facts about how the racial mix of schools have a huge impact on the kids attending. Anyone with half a brain knows this, but the SJWs will scream racism (as they're prone to do).

The author wanted to title this book, How Big is My Penis: What Google Searches Reveal About Human Nature. But my publisher was like, “People would be embarrassed to buy that in an airport.” That title would have been a more accurate description of the book. The author goes on and on about porn. He also goes on and on about proving his political views via the data searches he does. I was interested in reading this book because I wish to understand more about how social media and internet search engines collect and use data. This book was a big disappointment. I would recommend reading the following book instead, Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More by Matthew A. Russell The table of contents shows how fully researched and informative this book is on this subject and the reviews are excellent. This book received excellent reviews also, though, I have NO clue as to why.

I've only completed the first six chapters but have yet to read about any data that is even remotely valuable. Chapter Five was all about preferences of people on PornoHub a site I never even knew existed. Obviously the people who frequent that site are in a domain all by themselves. In an earlier chapter, he wrote about pancreatic cancer and how searches for "yellow skin" can be an early sign of pancreatic cancer. Actually, jaundice is a later symptom caused most probably by a clogged common bile duct and if you notice it, you've probably already made an appointment with your doctor. Nothing new here and no one will be saved.

My area of computer science is in artificial intelligence and I've yet to find any real intelligence from these data mining applications. They are basically expert systems which are a set of algorithms and rules based on knowledge engineering from an expert. IBM wrote an article a few years back titled, "Big Data, Big Nothing" and talked about companies having all this data but realizing very little information that is beneficial. Yes, sales and marketing companies will gain some insight but no true information benefiting the human race has yet to be discovered. The trick in using big data is to separate the merely interesting from the truly important. So far, most of these data engineers like Seth Stephens has only provided interesting facts.

I will keep an open mind and continue reading the book but hopefully will find some pearls of wisdom worth keeping.

I am not a fan of the style nor the asides in this book. The author takes bias to a new level with his potshots at Trump and his near hero worship of Obama. I get it, people have opinions, strong ones, but when you are claiming to be seeking empirical truths via big data you might want to set your personal hatreds and loves to the side. I strongly disliked how some data was accepted at face value because it validated what was expected, but then other findings led to more and more digging to explain unexpected, unwanted results. The author's obsessions with sex, baseball, racism, politics amounts to his own digital voyeurism. That's the basis of this book and it's not good in my humble opinion.

Scientists of human nature are vexed by the Law of Small Numbers - thinking that the traits of a population will be reflected in any sample, no matter how small. It's all the iffier when the sample is gathered by convenience. Now we learn that Big Data from internet searches and other online responses offer an unprecedented peek into people's minds. At the privacy of their keyboards, people confess the strangest things - sometimes because they have real-life consequences (eg. searches for professional advice), sometimes because they don't have consequences.

Author Stephens-Davidowitz has become adept at tracking the digital trails people leave on the web. He began with the 2008 presidential election and a long-debated question in social science - 'How significant is racial prejudice in America?' During Obama's presidency it became conventional wisdom that the overwhelming majority of Americans did not care that Obama was black when deciding whether he should be their president. Then the author found Google Trends - a tool released in 2009 that tells how frequently any word/phrase has been searched in different locations at different times.

Google Trends turns people's search for information into information. His effort to assess validity of Freud's attribution of sexual motivation to dreams (bananas are the 2nd-most dreamed of fruit - however, they are also the second-most consumed fruit. Similarly with cucumbers - the 7th-most consumed vegetable - and also the 7th-most dreamed of vegetable. Also disputed the rationale supposedly underlying Freudian slips (eg. typing 'cock' instead of 'rock') by simulating typing with the same rates of substitution errors as humans - the result was the same rate of sexual innuendos.

I read through the first two chapters and was not blown away. ”men are insecure about their own is size.” Really? How many? How many times was that googled. There are over 2 million people in the city I live and I am willing to bet searches about penis size Dont come close to the population of one American city. In fact, he never gave the actual numbers. In the two chapters I read he never once, said the number of searches for any of the words or phrases he is claims are ”popular” searches. Ensuring the reader can't quantify the data or its implications themselves. We’re forced to accept HIS interpretation of the data. For instance, he claims google searches prove there are ”a lot” of ”racist” in America. There is a difference between racism and desire for immigration reform. The people shouting ”build a wall” aren't racist because being Mexican (or any other central/southern nationality) doesn't count as a race. ”Women conduct searches related to the smell of their vaginas. ” No. I think adolescent girls do.

If you have doubts about the accuracy of opinion polls and assertions by people with titles/degrees, then you will find confirmation in this book.

So much that people expect you to believe is just "faith based" rhetoric. Although the author uses Google data to undermine Big Data, this data is based on those using Google on these narrow topics. A vast group is not even counted. So even his conclusions are suspect (an example is his estimation of the percentage of the population who are same-sex oriented).

He is correct that individuals use different words to describe the same thing. It's not just the English language but the entire country is factionalized.

Con-artists manipulate statistics to get a result that confirms what they are selling.

One of the most valuable lessons that Seth learned is that some people's "gut instinct" is more accurate than anything else. Just another example of how you cannot quantify everything.

It is a fact that people lie to "game" the system. Business spends a fortune trying to figure out ways to get your money. The latest theory is that tracking people will uncover the secret of how to manipulate you. But since humans are irrational, it's doomed to failure (Google are you listening?).

He points out a study in this book showing that many books are not fully read. It's hardly definitive, but its probably a fairly accurate study. One of the books that was not well read was the psychologist Daniel Kahneman's book "Thinking, Fast and Slow." This book on the scientific application of big data collections will be finished by many more people. Though Kahneman's book was more impressive, more scholarly and I believe, ultimately, more indicative of human behavior, people will just prefer Everybody Lies because it is even easier to read (and TFAS was easy but more detailed and less conversational), well written - if a little too hokey for me, fast paced and didn't tax the brain at all. As Kahneman explained, even doing a little thinking is an effort for people. We all know that feeling. Just ask someone to do some easy division in their head and most everyone panics or runs.

I am sure there is a tremendous amount to learn from big data. And Stephens-Davidowitz did explain the limitations and problems with it. Still, I thought that there were greater problems than he stated, e.g., the viral effect of searches, the question of who uses the internet, the question of how self-deceptive people actually are who make internet searches. And so on. I'm not saying it has no value. It may have a large value. I just don't think as big as he says. However, people will become addicted to it, especially as it gets easier to use, and it might replace some social sciences in some ways.

These are a few mental notes I made to myself that will not be spoilers. I thought his brief treatment of Popper was poor. I don't know that the availability of big data changes anything about the testability of Freudian psychology. I doubt he has thoroughly read Popper (just read about him) or Freud thoroughly, though I could be wrong. Because even if big data makes available some aspects of what we are looking for, it certainly does not explain even a fraction of what Freud wrote, which, if based on research and "sciency." was also, quite unscientific.

Was his commentary on his brother real, or was he teasing? I have brothers and it may have all been tongue-in-cheek. But, it really didn't come out that way. Maybe he was serious. Either way, he may want to re-write that part.

Like many books written these days, there was a strong liberal bias. I hate political biases, right or left, written into any book on science.

All in all, I recommend. It is the future.

Pardon me that my review title had been a little bit over but I really want to tell you how great this book is. When the term had become a cliche, or worse than one, it took an authentic expert like the author to burst that bubble of quasi intelligence that it is bigger or higher computing power the better, but the smart and somehow intuitive use of what we have. In short, a must read for any marketer or statistician. Highly recommended!

p.s. Below please find some favorite passages of mine for your reference.
Searches for jokes are lowest on Mondays, the day when people report they are most unhappy. They are lowest on cloudy and rainy days. And they plummet after a major tragedy. Pg19
At Google, major decisions are based on only a tiny sampling of all their data. You don’t always need a ton of data to find important insights. You need the right data. A major reason that Google searches are so valuable is not that there are so many of them; it is that people are so honest in them. Pg21
The data tells us that in worse off families, in worse off communities, there are NBA level talents who are not in the NBA. These men had the genes, had the ambition, but never developed the temperament to become basketball superstars…..In fact, anyone from a difficult environment, no matter his athletic prowess, has the odds stacked against him. Pg41
Offering up new types of data is the first power of Big Data. The porn data and the Google search data are not just new, they are honest (the second power). Allowing us to zoom in on small subsets of people is the third. Allowing us to do many causal experiments is the fourth. Pg53-4
“Gay” is 10 percent more likely to complete searches that begin “Is my husband….” Than the second place word “cheating.” It is eight times more common than “an alcoholic” and ten times more common than “depressed.” Pg116
On weekends with a popular violent movie, the economists found, crime dropped significantly. Pg192
Prisoners assigned to harsher conditions were more likely to commit additional crimes once they left. The tough prison conditions, rather than deterring them from crime, hardened them and make them more violent once they returned to the outside world. Pg235
Terms used loan applications by people most likely to pay back: debt free, after tax, graduate, low interest rate, minimum payment. Terms ….mostly likely to default: God, promise, will pay, hospital, thank you. pg258-9

Although many of the topics addressed in the book seem interesting, the organization is terrible. The author jumps from one thought to another with no clear direction. Also, the author seems full of himself and that comes across clearly in his book. He focuses more on his personal opinion and anecdotes than news regarding big data. Overall, I would not recommend.

I am an engineering PhD student who dabbles in data science. There are a lot of fascinating and useful applications of the subject. I hoped this book would cover some of them, and I was hoping to enjoy it. No such luck. It seemed that the author had an idea (the idea being that google search data will provide some useful new insight into human behaviors or societal patterns) and decided to write the book before reaching any meaningful or well-formed conclusions. Instead he relied on dumping out a bunch of speculations, spewing out lots and lots of observed correlations without thorough proof of causation (despite what he may claim), and talking ad nauseam about sex and porn -- because ya know, that usually sells some books if all else fails.

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz

“Everybody Lies” is an interesting angle on how we can reveal who we really are by analyzing big data. Former Google data scientist, BA in philosophy and with a PhD in Economics, Seth Stephens-Davidowitz provides readers with a stimulating look at whom we really by showing how Big Data is best used and explain in detail why it can be so powerful. This revealing 357-page book includes eight chapters broken out by the following three parts: I. Data, Big and Small, II. The Powers of Big Data, and III. Big Data, Handle With Care.

Positives:
1. A well written, and engaging book.
2. A fascinating topic, how big data reveals who we are.
3. Stephens-Davidowitz is a gifted and entertaining writer. This is a book that makes an interesting use of the Google tools and reveals some stunning insights. The author is not afraid to make some bold conclusions.
4. The introduction does not waste time to tease the readers. “Surveys and conventional wisdom placed modern racism predominantly in the South and mostly among Republicans. But the places with the highest racist search rates included upstate New York, western Pennsylvania, eastern Ohio, industrial Michigan and rural Illinois, along with West Virginia, southern Louisiana, and Mississippi.” “Areas that supported Trump in the largest numbers were those that made the most Google searches for the N-word.”
5. The basis of the findings in this book can be summarized in the following quote, “I am now convinced that Google searches are the most important dataset ever collected on the human psyche.”
6. Describes the four powers of Big Data. “There are many unique data sources, on a range of topics, that give us windows into areas about which we could previously just guess. Offering up new types of data is the first power of Big Data.”
7. The importance of being precise. “The Big Data revolution is less about collecting more and more data. It is about collecting the right data.”
8. Interesting insights on the use of Big Data. “First, and perhaps most important, if you are going to try to use new data to revolutionize a field, it is best to go into a field where old methods are lousy.”
9. The book is full of interesting examples. “Walmart suspected—correctly—that people’s shopping habits may change when a city is about to be pummeled by a storm. They pored through sales data from previous hurricanes to see what people might want to buy. A major answer? Strawberry Pop-Tarts. This product sells seven times faster than normal in the days leading up to a hurricane.”
10. Find out if a man and a woman will go on a second date based on how they speak.
11. A look at Facebook. “The fact is, many Big Data sources, such as Facebook, are often the opposite of digital truth serum.”
12. An analysis and examples of the second power of Big Data. “This is the second power of Big Data: certain online sources get people to admit things they would not admit anywhere else. They serve as a digital truth serum.”
13. A fascinating look at hate and prejudice. “On the one hand, the overwhelming majority of black Americans think they suffer from prejudice—and they have ample evidence of discrimination in police stops, job interviews, and jury decisions. On the other hand, very few white Americans will admit to being racist.”
14. The findings regarding abortion. “What drives interest in self-induced abortion? The geography and timing of the Google searches point to a likely culprit: when it’s hard to get an official abortion, women look into off-the-books approaches.”
15. A look at how corporations make use of Big Data. “Netflix learned a similar lesson early on in its life cycle: don’t trust what people tell you; trust what they do.”
16. Describes the advantages of Internet experimentation. “This is the fourth power of Big Data: it makes randomized experiments, which can find truly causal effects, much, much easier to conduct—anytime, more or less anywhere, as long as you’re online. In the era of Big Data all the world’s a lab.”
17. Big Data in the hands of economists. “The average movie in our sample paid about $3 million for a Super Bowl ad slot. They got $8.3 million in increased ticket sales, a 2.8-to-1 return on their investment.”
18. Words of wisdom. “People adapt to their experience, and people who are going to be successful find advantages in any situation. The factors that make you successful are your talent and your drive.”
19. Discusses the limitations of Big Data. “The problem is this: the things we can measure are often not exactly what we care about.”
20. A fascinating look at paying off debts. “You might think—or at least hope—that a polite, openly religious person who gives his word would be among the most likely to pay back a loan. But in fact this is not the case. This type of person, the data shows, is less likely than average to make good on their debt.” “Someone who mentions God was 2.2 times more likely to default. This was among the single highest indicators that someone would not pay back.”

Negatives:
1. EBook fails to link to the notes which is a must for books of this ilk.
2. Repetitive.
3. Some of the conclusions are expected.
4. Some of the conclusions may be a stretch.
5. No formal bibliography.

In summary, this is a fun social study book. Stephens-Davidowitz demystifies data science and along the way he makes some startling revelations. He shows data science is about spotting patterns and how variables affect one another. On the other hand, some conclusions may seem a bit of a stretch and the eBook doesn’t take advantage of the linking capabilities, which is a must for this type of book. I enjoyed it, I recommend it!

Further suggestions: “Big Data: A Revolution That Will Transform How We Live, Work, and Think” by Viktor Mayer-Schonberger and Kenneth Cukier, “Hit makers” by Derek Thompson, “The Predictive Analytics” by Eric Siegel, “Big Data Made Accessible” by Anil Maheshwari, “Data Smart” by John W. Forman, “Data Science for Business” by Foster Provost, “Big Data for Dummies” by Judith Hurwitz, “The Inevitable” by Kevin Kelly, and “The Signal and the Noise” by Nate Silver.

Surveys, exit surveys and questionnaires have a limit to how much information they can provide about human behavior. This book explores what online searches can really tell us about people’s intentions and true feelings. There is a lot of data that can be mined from large sets of data and just because someone searches for something doesn’t necessarily mean the person believes that way. Curiosity does not in and of itself reveal human behavior, but it can provide big picture trends that may not be captured in traditional ways.

The author explores a diverse selection of online sources and studies that open the doors of human intention. “Everybody lies” refers to the inaccurate answers that many people give to poll questions or other surveys that they may be too embarrassed to say in real life. This was highlighted in the recent Presidential elections in the United States where race, economics, gender and other factors weren’t properly captured by the polling data.

The author, Seth Stephens-Davidowitz uses myriad examples to show the revelations gleaned from big data. The words used to describe a relationship or about a group of people can have a huge effect on how that group is perceived. One example talks about the language used by Pres. Obama in a speech about Muslims. One speech focused on high-level points about working with and not profiling Muslims, which resulted in negative searches about that group of people. Another speech focused on specific Muslims and what they had accomplished. This provoked more curiosity from listeners and more searches about these specific people happened.

The one point that gives me pause from giving a 5-star review is the subjective conclusions that are often presented as fact throughout the book. Evidence of certain data is not actually enough to draw completely accurate conclusions. There’s much more to be down with big data and how it can be interpreted. The conclusions are interesting though and the book offers a perspective on a number of topics that will give you food for thought.

Very refreshing and eye-opening write up on the reality of "Big Data" (which is a controversial term in and of itself). Seth pulls no punches, with data ranging from Google searches to porn site views, to show us who we really are. It can be both embarrassing as well as relieving to see how common our taboo desires and fears are when we turn to internet searches to satiate them.

That being said, the book is a bit thin both on substance, and on size, and one can easily go through it in a couple of days. It would've been fascinating to see more insights into other fields (disclosure, I work as a business intel analyst so find the implications to be wide-reaching once explored), aside from the few topics he focused on.

I would be open-minded to future writings, especially with more substance and depth, and would still highly recommend this book to those interested in who we really are as a species and society, and why that anonymous outlet of the internet reveals things that we never spoke of publicly.

This was an interesting read as I feel statistical analysis 'can' take personal biases our of observation commentary. However, the author's comment: "One of the points of this book is we have to follow the Big Data wherever it leads and act accordingly", places too much blind faith in the value of data mining. Two points on this: correlation does not prove causation - and in the end data mining relies on a cornerstone of correlation analysis; and, one's persona can, intentionally or not, skew any developed observation. In this regard, there is an old observation that words are the way of the monkey, meaning that with the same facts diametrically opposing conclusions can be formulated.
After reading the book, what was vary enlightening and disturbing (which is not a criticism) but an observation of technology gleaned from the book, was the invasiveness of data mining on each of our lives (example, recommendation by the store on what you may like which is based on your clicks - I still like the store). Bottom line read the book with your analytical mind and enjoy the commentaries within it.

This book is false advertising. I thought this was a work by a thoughtful researcher. Instead, in the first chapter I discovered that it was nothing more than a text by a leftist activist posing as a researcher. In the first chapter we are insulted with the claim that Donald Trump won in 2016...coz racism!
It was absolutely absurd to come across that in the opening pages of a book that's supposed to be about psychology. Leftists continually debase the populace by making them locked in a worldview that dismisses all claims with isms like racism and sexism.
We voted for Trump because the government in Washington is obese and corrupt. Eight of the richest counties in America ring DC. Why are they so rich? Because they steal from us and give to bureaucracies.
Trump has already improved our prospects by insisting that each new regulation be accompanied by 2 cuts to existing regulations.
The business of America is business...not calling others "racist."
His tax cuts for businesses produced 1000 dollar bonuses for workers and a raise in the hourly wage for Wal-Mart employees from 9 to 11 dollars. He has done more for the working man than the Democratic Party ever will.
With immigration, the Democratic Party imposes no limits. It shuts off debate with "racist" instead of explaining how we benefit from having all of these people come to our shores. In my view, 320 million people is more than enough. Let's have a 5 year cease to immigration.
Oh, and leftist, if you're obsessed with racism than that tells us all we need to know about you doesn't it? As for me, I treat others as my equal. Give it a try some time.

"Do we want to live in a world in which companies use the words we write to predict whether we will pay back a loan?"

I received a copy of this book through a Goodreads first reads giveaway in exchange for an honest review.

This book is an interesting look at data and how companies use that data and ways these companies and public entities could be using it.

It introduces lots of interesting concepts I hadn't previously been aware of such as finding a twitter doppelganger, how companies can determine if someone will pay back a loan based on the words they use, and proof of an implicit bias favoring men and boys. There's also some unsettling concepts like how someone's sexual proclivities are determined for life around middle school and how many people are searching for incest porn. The fascinating concept behind this horrifying discovery is that despite what people may say socially at the end of the day unidentified data tells the real story.

This was a fairly easy to read book that introduces a variety of thoughts about data in a way that isn't overwhelming. I also feel like I learned things including how to track google search trends and other ways data can be used.

While this certainly isn't a comprehensive look at modern data tracking and the many ways data is used on a daily basis, it's a good read that presents some big ideas.

A Freakonomics for the modern age, this book explores the provocative notion that we can get more reliable information from people's Google searches and other online activity than from their answers to traditional polling questions. Author Seth Stephens-Davidowitz argues convincingly that people sometimes respond to surveys with the answers they consider socially expected or even aspirational, but they are less motivated to filter themselves before a search engine that can bring them whatever they ask of it. As a result, aggregate big data can provide insights on racism, political trends, sexual proclivities, and other sensitive topics that classical research methods might miss.

I did sometimes wish that the author had consulted more linguists for his discussions of language-related studies, but that's a larger critique for the text-as-data research field as a whole. Overall, I thought he made some sharp points about the research avenues opened up by big data, as well as the ways that the internet in particular can reveal discrepancies between who we claim to be and who we really are.

Seth Stephens-Davidowitz is a data scientist or someone who studies data for insights into society. As he explains, “This audiobook is largely about how data on the web can help us understand people.” For example, how was triple-crown winner American Pharoah selected out of all the other horses to be a winner. According to Stephens-Davidowitz, data about the horses physical characteristics figured into this process where in the past it had been pedigree.

With access to Google data, the author has drawn different conclusions about what people are doing and how they are acting in society. The title EVERYBODY LIES comes from the fact the data or actual actions show something completely different from what people will say when surveyed or asked the questions. The big data from Google and other places gives a much more accurate picture than we’ve been able to access in the past. I found this audiobook interesting and heard it cover to cover. Recommended.

W. Terry Whalin is an editor and the author of more than 60 books including his latest Billy Graham: A Biography of America's Greatest Evangelist

Yes, there are some interesting results from internet searches, but that doesn't make up for the numerous deficiencies. Where to start? While the author mentions the fact that correlation isn't causality, he pretty much seems to ignore that in most of his conclusions and interpretations (that may arise from the "I don't care how it work if it is a good predictor" attitude, but that doesn't stop him from regularly implying he knows why things work).

The title is pretty misleading. Most of the book isn't about people lying. And most the cases given along those lines have been well known for a long time so there is no news there. And while the hype is on "Big Data" much of the book is leavened with examples that aren't big data but rather routine examples of social experiments (e.g., what language during the first date is likely to indicate a second date). Indeed, there is a rather absurd claim that people engage in "Data Science" all the time (this given apparently to make the term more palatable; e.g., you are engaging in "Data Science", such as when you behave this way then you don't have as many friends). If this is the definition, then it is so broad as to be completely useless.

What the book appears to lack is any sense that it is heavily biased in multiple ways. For one can mine data relationships to support their own prejudices. Ask these question but don't ask those questions. Keep looking until I find what supports my view. The data results say this, but let me put my own spin on the results that isn't necessarily justified and then pretend that "Data Science" proves X. This author particularly plays fast and loose in this regard. Some examples. He cites a study of newspaper language to determine whether they have a liberal or conservative bias. Most of them have a liberal bias it is determined. But then it is asserted that the paper is giving the customers what they want. Their readers are liberal. So no problem! Well, hold on. Do they have a liberal slant because their customers are liberal, or do the liberal customers have the paper because it is liberal? It's that correlation/causation fallacy at play here. Or that because peoples use of the "N word" correlates with how well Trump did implies Trump is a racist (and that the country is horribly racist if a small percentage of people use that term). Again, correlation doesn't prove casuality or even impute attributes here.

And as an example what questions aren't asked, are whites more racist than blacks? There are substantial data to suggest that there is more animus of blacks against whites than conversely. Is racism a significant factor in why blacks do poorly? This is just a plain assumption on his part (admittedly, it is an article of faith in most of the academic community, without the data to back it up).

The book is permeated with the attitude that Obama was pure and wonderful, and that people that voted against him must have had bad motives. Again, no substantiation for that. It's just an article of faith. Many of the issues that he discusses that touch on political issues have this blinkered viewpoint. It sure seems like he is someone who is digging for data to support his view, and interpreting it to support his view. No real searching exploration of alternative explanations.

If that is what you want, then this book is for you. But don't pretend that it is objective "Data Science". God help us if this is the current standard for academic research.

Everybody Lies is both very well researched and written. I was reluctant to read it because it's a data book which means stats. Also an economist wrote it so as unpleasant as a stats book would be it would be much worse getting through the prose of an economist. I was completely wrong. Not only is the book very readable it is a pleasure. My stats avoidance response was completely unfounded. I like the idea of using Google searches and other internet based research because people are more honest if they remain anonymous. Face to face & telephone surveys have their function but they are becoming increasingly unreliable. If any of you enjoy the work from Nate Silver's website FiveThirtyEight you'll love Everybody Lies. I'll keep this book and will not loan it to anyone. I will talk it up but my copy will remain with me.
Buy a copy of this book and read it now.
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

I'm not an economist, and to call myself a lay person would be to give myself too much credit. I just greatly enjoy the Freakonimics podcast, which is where I heard of this author and book. I immediately ordered the audiobook.

First, be prepared for very offensive language. Much of the book focuses on personal Google searches, and people search for offensive stuff. The author does not sugar coat this.

The author has demonstrated that we don't really know our own people, and maybe not even ourselves. Opinion polls are dead to me now, after passing through Davidowitz's shredder. You know that people lie to pollsters, and I know they lie, but now it's been proven. Proving they lie is of little value. This book proves they lie, and goes on to uncover people's real views.

It also shows how we're being manipulated in this digital age. I logged onto the store just now, and my page looks a little different. "Looks like they're A/B testing me" I immediately thought. Not only does the author uncover our true views, but demonstrates how our views and behavior are being manipulated on a constant basis.

I read books like this to sharpen my ability to cut through misconceptions that I may hold, and see the world as it truly is. You don't need to be an economist to appreciate information like this.

And yes, Seth, I read the whole book, and I hope you enjoyed that beer.

The author ends this book by writing, “Too few of you, Big Data tells me, are still reading.”
Perhaps if you authors would write more engaging books, we would finish them.
Truthfully, I really enjoyed this book. I read every chapter. But even if a non-fiction book fails to hold my interest, I always read the conclusion. My reading habits aside, i disagree with the conclusion. Big Data is certainly an impressive tool. But it hasn’t suddenly made the ‘soft’ sciences ‘hard’ – the travails of String Theorists notwithstanding. I look at his stats concerning racism, human sexuality, and tax fraud, and I have to wonder, as with all sociological/economic data what it actually means. I know this is a hackneyed thing to say, but sciences that have as part of their subject matter the human psyche, will always be infected with values in a way that hard sciences are not. Web data can tell us a lot. But so can the sale of Viagra and opiates. So Big Data is yet another tool of sociology/economics – but not a game changer – IMHO.

The title should be Everybody Lies and I hate Republicans. The content is good and the authers ability to make data
science fun is quite good. However I cannot give 5 stars because he brings his political views that does nothing to help his case with data.

It's hard to imagine a book written mostly based on what people entered in a search engine, but the author ingeniously used this easy-to-obtain repository to find many things about us.

Obtaining an authentic source of data is key to any data analysis. He rightly argues that people are less truthful to their closest aides/partners, but disclose their honest feelings to a search engine. I'm guilty myself, so much so, that I'm cautious of what I enter in the search bar now.. well not quite, I'll probably continue being myself.

There's a disclaimer on what Big Data cannot do and how it should not be used. The author also warns us of jumping to conclusions just because our data set was large.

I thoroughly enjoyed reading this book and will wait for more books like this.

This book comes at a great time in our society where information is so easily obtainable and yet the masses don't fully take advantage of it. The author takes the reader on a journey through the explosion of data and how we can harness the output of information on a grand scale. Furthermore, the author shows the importance of how much information we truly know and then explains through data science how correlations and causations move towards the next step of understanding our culture. This book had the perfect amount of wit which added to the easy read and did not ever drag on about any one subject. I think this is a must read for this year moving forward. Science and research is a huge part of out culture and what we use to better understand ourselves. As the author states, "this is just a scratch on a scratch of the surface for big data."

I liked it, quite redundant though. Seems to be a little to focused on what people google about porn. Everything is based upon the assumption that your'e going to lie on a survey in an attempt to make yourself feel better or look better vs. your peers. However when you need porn and turn to google you're true self shows because you need to get your porn fix and you're not going to lie about what you want or need information on. This was an interesting contrast from how 'big data' is used in 'Weapons of Math Destruction". I liked the book but didn't love it

This book is terrific for anyone who enjoys data-driven psychological commentary. The author offers thoughtful commentary on the discrepencies between the way people behave online and purport to behave offline. Examples include: a test of Freudian theories using commonly made typos, predicting unemployment and voting habits based on the wording of Google searches, and the queries for gay pornography in parts of the country where there are very few openly gay people. The book shies away from the technical details, so you do not have to be a data scientist to enjoy this read.

Some of the reviews claim the book to be "left-leaning". I strongly disagree with that assessment - the author does a good job applying thoughful criticism / commentary to both liberals and conservatives, and everyone in between. I certainly wouldn't say it has a left-leaning slant, but rather a slant towards identifying the curious behaviors that differ from what people publicly say.

Reveals how the biases you bring to your dataset are more predictive of your conclusions than what the data contains. Thought it would challenge me with new ideas, instead was just a smug attempt to appeal to my confirmation bias.

I found out about this book from the social media entrepreneur Tai Lopez.
Absolutely great book.
Reads differently from many other social science books of today. No fluff, and clear, BIG data.
Written in a manner that takes the reader on a voyage, surveying the new and often scary world of Big Data.
The author is Harvard-trained, yet writes in terminology that even an average layperson can clearly understand.
The obviousness and sarcasm kept me laughing and interested, and I was not bogged down by the unfamiliar terms and concepts due to the author's careful explanation.
Make sure you read the book all the way through the conclusion.
(Personal praise for the author: after finishing the book, I tweeted him with praise for the job well done. He actually tweeted me back thanking me for the praise. This is not something I was expecting, but something that definitely made the book's experience even more worth while. I'm not saying that he will tweet everyone back if you try because he is probably very busy. I include this in the review to show that Stephens-Davidowitz has an atypical and friendly character for an accomplished author.)

What a complete waste of time. The author breaks absolutely no new ground. It's like a cross between a Dan Brown novel and a John Tesh radio show; full of small factoids that ultimately amount to nothing. Spoiler alert! It's about the law of large numbers and our ability to collect data quicker. You can collect all the data you want, and ultimately all you have is a lot of data. You can try to divine human behavior from it, but people are unpredictable, and it's oftentimes difficult to know their motivation. A lot of the author's assumptions are inane, and clearly left leaning. Save your money.

What a fantastic book! There was something here for me and my wife (whom I read chapters of the book to before going to bed). So interesting and, I daresay, surpasses Freakonomics because of how Stephens-Davidowitz's uses big data and powerful analytics tools available today and not available when Freakonomics was written.

It's well-written and understandable for those who aren't data science or economics nerds but one can see how much thought and research went into writing it.

This one is a real page turner and gives enormous insight into our "real" selves. Despite the fact that everybody lies, I'm not lying when I say GET THIS BOOK NOW YOU WON'T REGRET IT!

I could not put this book down and became one of the 3% ;)

Everybody Lies by Seth Stephens-Davidowitz is basically Malcolm Gladwell using internet data research, but not as organized. I do not get the impression that Stephens-Davidowitz has ever written a full-length book before, because his Sections are divided into Chapters that often have their own sub-sections that tend to go off on tangents. So, while Everybody Lies is full of reasonably amusing (and sometimes rather disturbing) anecdotes of things discovered by spending one's life analyzing Google Trends, the entire work reads like a guy telling "Guess what!?!" stories at a cocktail party after one too many beverages.

Over the past couple of years it seems that it is increasingly difficult to make reliable predictions. Polls seem to totally miss the boat - witness most analysis of the 2016 US elections. Everybody Lies - a fascinating new book by Seth Stephens-Davidowitz gives us new insight on how to tackle numerous thorny questions with "Big Data". Stephens show how critical insight can be teased out of huge databases including what searching Google might just tell us. Stevens also discusses limitations and cautions in interpreting the results of Big Data.

Stevens style keeps you interested from cover to cover - including wanting to pour though the numerous notes to learn even more. One topic Stevens tackles with Big Data is race relations in America and provides some interesting information that is likely to challenge your existing intuition. Insight that could become increasingly helpful in informing our discussion following the tragic events in Charlottesville VA.

Big Data is an analysis process that we will surely be hearing more about in the future. Stevens book is a great introduction to what it is, how it is being and can be used - all delivered in an entertaining and thought provoking style. I highly recommend it.

This book is an easy read. Topics covered are interesting enough to make me finish the book in half the average time I take to finish a book of this size. My only complaint is that the book could have been more detailed. The succinctness of the book seems to have connection with the statistic (for which Seth ironically quoted the small sample size) that people doesn’t usually finish books written by economists. Another part explanation is his announcement of the sequel of the book in conclusion.
To make up for the lack of details, Seth referenced number of books & studies by other authors which gives adequate answer about “where to go from here” to the reader. His book certainly raised my interest in big data and gave some fresh perspective on how vast and unexplored the field still is.
I think Seth tried too hard to be politically correct. Most of the time he presented his finding purely in terms of data but his cautionary explanation of women seeking for violent sex appeared as if is too much worried about feminist backlash.
On one hand, his use of big data puts my mind to ease that how social data generated on Facebook and google has been put to good use like detecting child abuse early and preventing crime. On the other, he himself pointed out how easily could it all be misused to judge people’s IQ or job suitability on totally wrong (or even right) grounds.
Seth stated Freakonomics as his ideal book and his desire to have this book to be the successor of it. It is great to have such a lofty goal and the book was definitely close to Freakonomics in entertainment value. I’d still value Freakonomics higher on originality, shock content and somewhat deeper explanation of analysis.

3.5 stars: I learned a lot of random, sometimes amusing and sometimes sad things but could never piece together the author's rationale for what to include and where to put it, nor did I pick up very many principles about big data, aside from that it's best to get info from multiple complementary sources before forming conclusions (knew that already) and that people are often less than transparent when they know they are providing particular pieces of info about themselves (duh!).

I'm not much of a statistician, having long ago realized how people can make numbers mislead others, and how much fun it is to quote "statistics" I've made up for a given occasion. But I deeply appreciate thedescriptions of Big Data, how it can be sourced and inform, and has been, and how to size up race-horses. In short, this's an amusing, deeply informative, wide-ranging report on statistics, how to find them, how to use them, and how they've been used, especially in this age of digital availability and Google search results. A good book to learn from, a good book to pass the time enjoyably.

Full disclosure, I came into this book with a love of data analysis. Though I'm not really a data scientist, I'm an ecologist who analyzes data of various kinds as a profession. Regardless, I thought the author did a great job of making this information available to a wider audience. The topics discussed were highly varied, ranging from baseball to racism to porn, so it is certainly not just for math/technology geeks. Though I don't think anyone is going to read this specifically for the humor, I certainly chuckled aloud several times as I was reading. Seth's writing style is casual and conversational, again, making it a very easy read. I was fully engaged throughout the book and was regularly sharing bits and pieces with my wife as I read. I would highly recommend it.

1st criticism: The author does not attempt to conceal, or even temper, his political leanings. Though I did not necessarily disagree in this regard, I was concerned that it might alienate certain audiences. For the most part he stuck to the facts, but there are occasional snarky comments aimed at people that disagree with him. However, perhaps he should be praised for not shying away from these sentiments. Perhaps a scientist writing for the general audience should be granted such liberties.

2nd criticism: I would have liked to learn more about how specifically he is able some of his conclusions from the google search data. Specifically, he seemed to be able to separate google search data into demographic groups, which suggests he had information about the people conducting the searches. I wouldn't think that data would be available, so there must me some indirect analysis to get at that. There was a little bit of discussion about this sort of thing towards the beginning of the book, but I think it could have been more clear. Granted, its probably difficult to discuss these sorts of methods without getting very technical, very quickly, and perhaps that's why he kept it brief.

Regarding the overall theme of the book, he makes some pretty bold claims about the utility of these new data sources. Some other reviewers felt these claims were overstated, but I do not share that opinion. I think the author made a fairly solid case for how these data could be used to answer new questions, and answer old questions in different ways. Of course there are limitations and of course it is important to think about why certain patterns are observed in these data, but I was certainly convinced that there is a wealth of important things to be learned by this emerging approach to viewing the world.

Given that the foreword is written by Steven Pinker, I was pleasantly surprised that I was convinced by almost the entire book. (I am currently reading How the Mind Works by Steven Pinker and it is incredibly outdated and I am not convinced by much of it, but rather can point out many erroneous claims in How the Mind Works.) No wonder in the quotation from the foreword of the book (of which I skipped) printed on the back cover said that Pinker had his preconceptions turned upside down.

This book is very interesting and gives the so-called soft sciences a hard-science feel. Sometimes, it did feel that the author incorporated too much content about taboo topics in an effort to make the book more interesting. Overall, this book was interesting and unbiased, so I felt happy reading it in its entirety (except the foreword!).

This book is somewhat similar to freakonomics, but uses mostly large boring internet data sets to draw conclusions about everything from sports to politics to racism to porn fetishes. What makes this a great read is that the author happens to be an excellent writer. Stevens-Davidowitz is masterful at taking boring data sets and finding fascinating, sometimes salacious, nuggets of truth that give unique insight to the realm of human behavior.

In fact, a portion of this book might conclude that you will openly declare how distasteful the porn preference analysis is, but the data shows that you're lying - and you'll probably like that part the best.

The authors genuine enthusiasm for the future of data science and its potential impact on what we think we know about the world is contagious. Buy the book. Give it a read or listen. No matter what genre you prefer, this will leave you more insightful than it found you.

Really fun read, tackling some quite heavy, sometimes disturbing subject matter in an optimistic, highly readable tone.

I'm not and statistician, so I can't say for certain how verifiable some of his claims are - a lot of the time they rely pretty heavily on mere correlation, for instance. Also, much of the time the ambiguous meaning of the phrases are not properly unpacked, e.g the "is my (son/daughter) gifted" section seemed to neglect to examine just how ambiguous this phrase is. Stephen-Davidowitz assumes that the parent in question thinks their child is of superior intelligence, and since, in general the more common search is for "sons", it is boys they assume to be smarter. However, it's possible that parents only search this when they're uncertain of they're child's intelligence (that's why they're asking google!) and that it is less necessary to ask this of girls since, as, Stephens-Davidowitz notes, they are doing better at school. Also, the word "gifted" does not simply suggest intelligence - it could suggest a savant, suppressed intelligence that belies their day to day activity (and media presentations of such figures do in fact skew male, possibly due to autism also skewing male). So, I thinks suggesting sexism as the sole cause of this overlooks just how much information is missing from a google search - we don't know WHY they're searching it, or what they specifically mean.

Despite these reservations, it's a very fun, smart and provocative book that gets you thinking on a range of issues in an interesting way.

Data is still viewed as too high-level to understand and too mysterious to be invested. Seth has done a service to data scientists and others who show the value of something or lack thereof using numbers and digital tools. He demystifies and brings down-to-earth how to understand data in the lens of a typical internet user. That's no easy task and Seth writes in interesting and light ways, not the usual heavy-handed and lofty ways people might be used to when reading other books or articles on data and the Internet of Things.

Set to be the next Freakonomics no doubt. I highly recommend this book if you want to learn more about how data can help you do your job, your business or someone you know interested in the Internet of Things and numbers. :)

You may have already heard some of the author's results. It is astounding that certain Google searches can correlate with how an election actually turned out, while the polls missed by just enough to get the wrong answer. It is heartbreaking that racism was the key correlation.

But the broader scope of the material covered is also enlightening and fascinating? How many people are gay? Why do you root for a certain team, even if you no longer live in their city. What makes a great race horse?

And does everyone lie?

Stephens-Davidowitz definitely got my attention with this book. As a product designer, I've been doing a lot of reading on big data and the potential for machines to provide insights, so the sub-title initially caught my eye. What surprised me is how far apart traditional data capture methods are with current human behaviors and attitudes. A person might respond to a survey question one way and then go behave in the opposite way. Seth digs into search engine queries, website activity, and anonymous reports as a way to directly measure what people are really spending their time doing. I recommend reading how he and his colleagues find these amazing insights.

I was really excited to read this (and waited for six weeks for a copy to be available from the library) but....there's just nothing there.
Every time Mr. Stephens-Davidowitz claims he's going to give us a fantastic new insight, it turns out that it is not so new at all. Even if at one point it might have been fantastic.
He also repeatedly states that it doesn't matter if data shows correlation or causality -- the correlation is all that matters. I would argue strenuously that is flat-out untrue. There are certainly some cases in which it doesn't matter, but since so much of the "promise of big data" is that it is going to improve how we live our lives, it's imperative that we understand the causality, not just see correlations. You can't make policy prescriptions based on correlation. Period.
There is also (to my mind) an unhealthy focus on pornography and Mr. Stephens-Davidowitz seems quite certain that pornography websites hold the key to an understanding of human sexuality. I do not believe there is a whole lot to be learned about normal human sexuality trolling through porn site data. I am open to arguments about that, but none are made in this book.
Even though the author admits that it is very important to have reliable data sets, his assumption that people are (mostly) honest with their browser's search box and that online behavior is a great proxy for private offline behavior is presented as a given.
I'm also troubled that the author seems to have no qualms at all about manipulating internet search results or who knows what else we see on the internet in order to conduct "better" sociological testing. This is generally done without the knowledge of the "test subjects" except as when disclosed in one of those lengthy service policy or privacy policy web pages that no one really reads, even when they are prompted to check the "I accept" box. I find that morally repugnant myself.
I'm not sure why this book gets so many great reviews. I admit that I haven't (and won't) finish the book. Time is precious and I can't waste it reading about porn search queries and supposedly fantastic breakthroughs that really aren't.

I could not wait to get my hands on this book. Mr Stephen-Davidowitz details in his conclusion to Steven Levitt's Feakonomics and how he reacted to its plication and hopes some of his audience will have the same reaction. The analogy between the two books is fair on a high level, but Everybody Lies moves beyond Freakonomics once the author details the power of big data on the micro-level using Canadian hockey fans' devotion.
Mr Stephen-Davidowitz provides great entertainment and education for all audiences. His background into his logic and method are very approachable and provide informative detail for any with a background in running or analysis any social/scientific studies. He applies his raw data analysis into some popular and widely-held anecdotal "truths."
It is a very quick and engaging read.

BUY THIS BOOK. Whether you're a data scientist, a marketer, a student, or simply a human, the data analysis is insightful, to say the least. If "data" makes you hyperventilate, don't worry, Seth makes this easily accessible to those who understand the jargon and those who are just being introduced. The anecdotes aren't always pretty (read: humans can suck) but they are all backed up with data and research which makes them more important than anecdotes you get from Twitter.

Oh, and there's a lot of sex data in here, in case you're curious about more than how racist we are, where to birth and raise children, and how to get into the NBA.

I suggested my colleagues and especially my team read this book. I strongly suggest you do too.

"In his book, Zero to One, Peter Thiel, an early investor in Facebook, says that great businesses are built on secrets about nature or secrets about people....Thiel defines 'secrets about people' as 'things that people don't know about themselves or things they hide because they don't want others to know.' These kinds or businesses, in other words, are built on people's lies."

While this idea may be nothing new under the sun, anthropologically speaking, (hearkening back to the worlds' oldest profession), it was still revelatory to me to hear it for the first time backed up with data.

If you are looking for a great entry level book to understanding BIg Data, this is it. For those of us who don't speak this language on a daily basis, it was easy to understand, full of timely and relevant examples and while not a book I could recommend to my conservative Mother who blanches at many of the topics therein (we discussed the 30k merits) it was a wise investment of my time.

Highly recommend.

The premise is simple: People lie to other people, but not to Google. Pulling data from Google searches presents us with a picture that is more accurate than any survey could project. Many of our assumptions about the nature of humanity could be far from perfect, because we've never had such a precise tool for reading the honest, unabashed thoughts of other people.

Stephens-Davidowitz outlines his case for the use of Big Data versus outdated measurements of human ideology, and how this Big Data can bring our understanding of human nature out of the fog.

His subject matter is focused and centered on the data, but his humor is sprinkled throughout the book, especially when he is self-deprecating.

It's a quick read, whether you're a data scientist or a layman, because he lays out his case with coherence. It's not mired in confusing facts and figures, nor is it missing illumination. The Goldilocks of books, it's a fun and relaxing read.

The initial premise is interesting, and the title is catchy, but I find the execution extremely disappointing. The author doesn't dig deeper than the superficial level (letting others do the real exploration following his revolutionary opening) and makes the same point over and over ad nauseam, in the tradition of long winded modern American essays for which you could summarize in ten pages what the author dilutes into three hundred (because the publisher couldn't charge $25 for ten pages). What's left is a set of quirky but inconsequential observations, some juicy bits about sex, and a lazy, uninterestingly and uninspiredly meta conclusion. A couple of extra nitpicks: 1) the audio version sounds like it was a bit rushed, the narrator often putting emphasis on the wrong words; 2) the reference to Freakonomics with the repeated mention of one of its two coauthors without ever naming the second is downright stingy.

one of those books where data,human patterns, behavior and other factors bring in the results
that this book concludes to. some of it will make you laugh, some of it will disturb you, some of it will make you look around
and wonder if you are being spied on, and in between it makes you wonder what kind of world you truly are living in. alot of things
to put together and digest.

I pushed through it, since I enjoy reading about research and data. However, in terms of enjoying the writing itself, I did not. I found the author’s attempts at being funny/clever to be alienating and snarky. (And I typically enjoy sarcasm and dry humor!)

Also: nothing can be highly significant in terms of statistical analysis. It’s either significant or not based on a threshold.

Perfectly crafted and fully engrossing, "Everybody Lies" delivers a fresh overview of big data with an emphasis on the intriguing insights revealed by Google search trends. These trends, combined with other organic online resources, are an embarrassment of riches within which to unearth sociological insights. The book also draws a new perspective on the power and peril of deployed machine learning (calling it "doppelgänger discovery"), as well as the ever-important pitfall of p-hacking (referring to it as "the curse of dimensionality"). The book wraps up with a hilarious and poignant conclusion at the very end, whereby the author himself practices what he's been preaching throughout.

Eric Siegel, Ph.D.
Founder, Predictive Analytics World
Author, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

A young idealistic author that needs to wait a few years so he doesn't think obvious statistics as ground-breaking. The entire book is a discussion through a lens of current political correctness, which made it one of the most painful books to finish. Essentially he makes causation links to whatever supports his inexperienced political views, while slicing the data enough to declare views he doesn't like as correlated to idiots.

I am an older guy who does not have a great touch on mathematics.
Read this book twice and I feel it put me on track to deal with some data scientists I now have to work with.
Actually, could come up with several suggestions for them on big data that they liked and used!
Thanks a lot for helping me to do my job at my age in this age.

I gave it 3 stars because it had some interesting topics. However, I feel that it is not worth more because of a couple of things. First, it largely is about the data the author obtained from either Google or Pornhub. As such, there are moments in the book where the author really dwells on topics for a long time. More importantly, though, was that I felt that the author was either jumping to a conclusion from the data or encouraging the reader to jump to a conclusion that did not make sense. For example, the point is that everybody lies, so to exemplify that he talks about how both women and men offer differing statistics regarding condom usage. Then, he says they both lie because condom sales show otherwise. This is frustrating thinking because sales do not equate to usage. Some condoms may be saved for later, or they may expire. Additionally, it felt as if the author ignored the fact that condoms could be used for same-sex relations, too, so the numbers between men and women do not have to be equal. I saw this later in the book, too, when the author implied that the true percentage of gay men was 5% due to the number of searches he was seeing, but ignoring the fact that any type of person could search for gay pornography. It just felt as if there were too many instances were conclusions were being jumped to.

This should be required reading for all of us who have suddenly found ourselves in a data-centric universe. It raises excellent questions about who we really are and how we should be thinking about our current predicament. The book is fast paced and full of fascinating anecdotes. Sometimes it feels like there are too many non-sequiturs, but the point is driven home regardless.

And because no one reads reviews, I am simply going to conclude in a manner appropriate to the author himself.

"I am going to get a beer with some friends and stop working on this damn conclusion. To few of you, big data tells me, are still listening." - Seth Stephens-Davidowitz

Best book I’ve read in 2017! It was not only full of fun stories related to big data, but it did 2 things for me. 1) it helped me better understand how important big and small data are and how I could use it in healthcare and 2) it taught me more about the powerful resources and tools at our fingertips like Google trends. The author is not only gifted in his experience and his writing, but he’s also quite funny...what an excellent read, ALL the way to the conclusion!

Everyone should read this book!
He approaches "big data" in a way that everyone should be able to really understand. He teaches us about all the details about our indivuals lifes and is collected from our searches (serious and otherwise) via google and other search engines. He does it with wonderful examples and intelligent sense of humor. A pleasure to read and learn from.

Excellent.
Fascinating how big data can be coaxed to differentiate between what people report compared to what they do.
I learned a lot about behavior.
P.S. Seth, I did finish your book and would have liked to have read your well-composed conclusion.

If you are considering this book, which I assume you are if you are reading this review, go for it. It does not disappoint. There are so many moments in this book where you get to say "ahhh that actually makes pretty good sense!" This is light enough to pick up any time of the day, and just technical enough to satisfy any info junkie. Stephen does a wonderful job at reminding us just how human we are, while revealing a lot more about ourselves as group than we'd care to admit. In a lot of ways this book can be seen as a looking glass that lets you peek into the world that we actually live in.

While it sometimes steers too much into a defense of Big Data, “Everybody Lies” is compelling and thought-provoking in forcing the reader to consider hidden racism, sexual desires, the ethics of Big Data, and the ultimate question of why we are prone to subtly lie to everyone but Google

This book uses different researches and approaches a Research Question from different angles. First explaining the potential bottlenecks, how these can be prevented and what the outcome of the research was.

This book is eye-opening, flabbergasting and contains potential laugh-out-loud moments, this is by far my favourite book I have read in the past year and I cannot recommend it strongly enough. It provides insides into the human psyche as we have not had prior to this research and the potential of big data is discovered.

If you want a book that will give you astonishing facts, easy to understand explanations and a deeper understanding of the human psyche, start with this one, it is exceptional.

There have been a handful of books written on the new phenomenon of Big Data, and this one's the best. An eminently readable, funny, and accessible look at what the data can tell us, rather than an an anodyne, report on the fact that Big Data is a thing now. The third section, on the shortcomings & ethical questions of big data, is frank and, pleasingly, doesn't feel like it's something tacked onto the end.

Stephens-Davidowitz is one to watch.

Fascinating. Very readable. I think chapter 4 depressed me to no end, but I read that a few weeks ago, and the memory is fading. I have a feeling this author might read his the store reader reviews. If so: Big data tells us people don't finish books by economists. I believe this book will prove the exception. Hell, I even read the footnotes.

Great read for anyone interested in the way we as a general population thinks. Even for the casual reader, unfamiliar with the emerging fields of behavioral economics and data science, Stephens-Davidowitz does a great job introducing these concepts and expanding on them with fascinating research and analysis. Big data is here to stay and will be taking over more an more of our everyday lives, this book is a fantastic foray into its benefits, limitations, and implications on society moving forward.

I NEVER read. NEVER.
However, this book was so interesting I couldn't put it down. I never thought about how our Google searches are a reflection of our true selves, and how we can use all these searches to create meaningful correlations that can contribute to society. (ex. increase in google searches for "suicide" in a certain city could benefit from an increase in suicide prevention ads online or TV in order to help those in need.)
S/O Freakanomics and Seth Stephens-Davidowitz.

An interesting read. For me it was sort of an eye opener into a new way of looking at and thinking of using available data to reach new and better understandings. Problem is that the author has a completely different axe to grind and do so throughout the entire book.

There is something the author repeatedly focuses on which for me just seems forced and illogical. He's focus on how bad islamophobia is. Why is this mentioned time and time again? The author gives no background for why such a view is unquestionably bad. Why is islamophobia so bad? He mentions antisemitism and racism. These are believes/views we -know- have caused unimaginable pains and suffering. However a fear of or an unfavorable view upon Islam seems to me to be quite healthy. If you wholeheartedly disagree with me you choose(?) to not consider any facts. At least if you disagree with me, like what the author does, at least explain your point of view. Just for a paragraph or two.
In his book the author view any area of interest from different angles. He tries different perspectives to reach a conclusion. On islamophobia there is no different angles. No alternating views. No diving into the depth of data to see if there is a reason for people to have or to form such an opinion. Shouldn't that be an obvious thing to start with? How/where did fear of islam become unquestionable bad? Should the author's personal view just be taken as a fact?
The apex of this peculiar recurring viewpoint is at the end of the book where the author starts talking about usage of Big Data in preventing crime. He then does research on for example "kill muslim" seemingly taking it for granted that islamophobia is the path to this. This he relates to a number of 12 killed muslims. (Not) surprisingly the ratio of searches compared to deaths are extremely high. So high there seems to really be no correlation at all. The author also also seem to fail to mention that these deaths could be unrelated to any "islamophobic crime". It's just such a weird area of focus. Why this obsessions with fear of islam?
It is an obsession. The natural thing to do after doing the research on "kill muslim" and that would also be following the research done for the other subjects in this book, would be to research the opposite. See if there is a rationality behind people actually looking unfavorable upon Islam as a belief and an ideology. Where is the research on "jihad", "do jihad", "commit jihad" and etc.? While writing this, 23rd of May 2017, I'm right now getting a newsfeed from a terrorist attack in Manchester, England. At least 20 people including children killed in a bomb attack. Could Big Data have prevented this? Probably not by looking at searches made for "kill muslim"...

Dates related to this subject from Europa, but not motivated by islamophobia:
* Manchester, 23. May: 20+ people killed
* Stockholm, 7. April 2017: Fem people killed
* St. Petersburg, 3. April 2017: 14 people killed
* London 22. March 2017: 5 people killed
* Istanbul, 31. December 2016: 39 people killed
* Berlin, 19. December 2016: 12 people killed
* Nice, 14. July 2016: 86 people killed
* Istanbul, 28. June 2016: 36 people killed
* Brussel, 22. March 2016: 32 people killed
* Istanbul, 12. January 2016: 13 people killed
* Paris, 13. November 2015: 130 people killed
* Paris, 7. January 2015: 12 people killed
* Paris, 9. January fire people killed
* Istanbul, 10. October 2015: 102 people killed
* Istanbul, 20. July 2015: 33 people killed

Then we have the number of people killed in all the ongoing conflicts between jihadists and society in the middle east. Probably hundres of thousands. Millions of refugees. Then we have the number of people killed because of islam and islamic rule.

Another list:
1. Afghanistan
2. Iran
3. Malaysia
4. Maldives
5. Mauritania
6. Nigeria
7. Pakistan
8. Qatar
9. Saudi Arabia
10. Somalia
11. Sudan
12. United Arab Emirates
13. Yemen

These are countries where non-believers can be sentenced to death. For being non-believers! But have no fear. We'll focus on the world altering problem of islamophobia. Big Data will help us in this fight. Fear of Islam as a religion and ideology is totally and utterly unfounded. Where could anyone anywhere have gotten such a notion? The author of this book has spoken!

Although this is a very serious and grave are there is a funny side to this book, related to the author's focus on fear of Islam. He spend a lot of time talking about president Obama's speech after the shooting in San Bernardino, California, 2015. Obama wanted to address the country and hopefully try convey the notion that the government could both stop terrorism and lessen islamophobia. Or as written by the author, "government could both stop terrorism and, perhaps more important, quiet this dangerous Islamophobia". The author probably and hopefully does not mean this, but what he writes almost reads like the fear of Islam is worse than (muslim) terrorist killing people(?)... The author then does a research on the result of Obama's speech and if it did in fact result in a better understanding between different views, and if it did induce tolerance and inclusion. It did not (according to the author). The funny thing is that this book does the same regarding this (important) subject for the author. His focus on the fallacy of thinking Islam is something to be feared does in fact in no way lessen my increased sceptical view on Islam. The author does nothing to explain his viewpoint. Just state a "fact" that Islamophobia is bad. So bad that the obvious thing relating to Islamophobia is searches for "kill muslims". In fact this fear is so bad that there does not seem necessary at all to actually dive into the data and see if there is something more to this. We should just take his view as a fact.

A smart, witty, and important book. Despite what is suggested by some conservative reviewers of Everybody Lies, we do know that the current political climate has brought to the surface a lot of hidden tensions, fears, and hatred--stuff that we might not have quite understood the true extent of before now. (Or most of us didn't. It seems Seth Stephens-Davidowitz did.) The author uses a novel and forward-thinking approach to determine what's simmering beneath the surface of the human psyche, as well as where and when that simmer might come to a boil. As he demonstrates, this new method of collecting information can be used to help map, predict, and possibly even quell bigotry and hatred. You'll also gain lots of fun and unexpected insights on sex, violence, and consumerism. This book skillfully (and readably!) demonstrates that we have another perspective on the world we've been ignoring. It might change your perspective on a number of things, too.

Just read it. LOVED IT! If you are into data science and/or enjoyed books like Freakonomics, you will enjoy this book. Unlike many books on economics and related topics written for a general audience, this book is WELL-written. Buy without hesitation.

This book is fantastically compelling. I am amazed, delighted and fearful of the power Big Data. This book only made me want to learn more, think more, understand more and I believe it has changed the way I will live the rest of my life.

On page 280, Stephens-Davidowitz writes: "I hope this book might have the same effect on others that Freakonomics had on me." In this quote, Davidowitz is referring to the curiosity that resulted from reading Freakonomics and its ultimate impact on his life (going into data science and writing this book). While I have not switched careers (yet) or composed a best-selling book (yet), I have begun to change the way I think about the world and even changed my approach to my job. If you want a book that will spark your curiosity, awe you with unpredictable facts, and keep you laughing along the way - I highly recommend this book in its entirety.

The end caught me off guard. I was waiting for that one last profound zinger, and the zinger was.... Funny. By the way.... Made it to the end and enjoyed it all the way through. Thanks for an role-playing read!

I purchased this via Audible on the advice of a neighbor. It was really interesting, but after about 45 minutes I couldn't stand the repeated slamming of Trump supporters as being racists. I'm sure some are, but I'm also sure some aren't. I got so sick of his biases that I lost interest in hearing what he had to say and returned the book to Audible.

One big hole I found in his research is that he portrays what people type into internet searches as being representational of societies at large. This is a failure of basic research gathering, as if under-represents those people who don't simply don't use computers very often. It's a bias to assume that all people go to the internet for information and answers. Believe it or not, there are plenty of people who don't, such as those who have limited access to computers, like many immigrant groups and the very poor.

Great book. If you like the Freakenomics books or books that tell great stories using data while offering a chance to learn something, this is the book for you. I can’t wait for the next book “Everbody (Still) Lies”.

Just finished reading Seth Stephens-Davidowitz's wonderfully entertaining book "Everybody Lies". I was one of the lucky ones that put a hold on it at the library before everyone else realized what a great book it is. I was a Navy cryptologist before becoming an emergency room doctor and I'm always looking for ways to override surface truths and separate the signal from the noise. Seth does this in a great way, depicting many examples where the truth of the matter was not what I thought it was. He does this in a humorous, entertaining way that makes it easy to keep reading to the next chapter and hard to put down. Anyone that enjoyed reading "Freakonomics" or "The Signal and the Noise" will love this book. It may even help you decide whether to loan money to that needy friend of yours or whether tonight's first date will lead to a second.

This book has some interesting information, but its content is mostly peripheral to the practices of big data analysis. I worked through several courses in big data analysis. and almost none of the content in this book was represented. Big data is about using computer power to apply algorithms and statistical inductive techniques to huge data sets in order to classify, identify, and predict. Analysts also employ exploratory techniques to search for patterns in large data sets, and build the recommender engines that recommend products and movies to you online. Some of the techniques require some application to comprehend, i.e., penalized regression, etc. A non-technical book can't really convey the essence of big data analysis. By the way, the author indulges himself in irrelevant PC virtues signalling, if that annoys you.

Wow. One of the best books I have read (and I read a few). Fascinating insights on the human race beautifully articulated and with the perfect tone of voice.

A must read for absolutely everybody living in an age of big data, social media, Google and a lot of misperception/lies.

Make sure you read it all the way through to the perfect conclusion.

Did you like "Freakanomics" but think it might contain just a few too many just-so stories? Give "Everbody Lies" a go. It's an evenly paced (not all the good stuff is in the first 50 pages) and even-handed take on the utility and limits of big data. There's a new field of scientific inquiry brewing among all these bits and bytes and Stephens-Davidowitz offers and excellent primer on what's been done and what may be left to do. Very likely one of the best popular science non-fiction reads of 2017. The high school science teacher in me would be remiss if I didn't mention that mature themes are addressed in parts of "Everybody Lies". So, judge for yourself if you plan on giving this excellent book as a gift to your nerdier friends.

While this is a very interesting book, well paced, and a 2017 relevant topic, Stephens-Davidowitz makes a few irritating decisions that turn this book into a two-star turn off:

#1 - His personal liberal bent gets in the way of any true academic research. For example - he presents his findings as hard proof evidence that bigots elected president Trump and States that don't allow abortions put more humans at risk (as a side note, read or reference the book "How To Lie With Statistics by Darrell Huff to note flaws in this kind of data presentation). His culling of data is hardly to be discounted but throw in as many "Take that Trump!" statements and one has to wonder if he's not jimmying the results to support his personal bias. Could it be the neglected middle-class (who also uses horrible language) voted Trump into office and the States with more open abortion laws actually do more harm to human beings (i.e. how many unborn children are executed vs the number of at-risk adults through "clothes-hanger" abortions).

#2 - The data presented here is all HISTORICAL - every last bit of it. And contrary to the adage "history repeats itself" and the knowledge that history can be a good indicator of the future, it does NOT 100% predict the future. Again, the liberal slant of Stephens-Davidowitz argues that Trump administration immigration policies are wrong (another "Take That Trump" inserted by the author) when he sets to show that more famous people come from immediate families of immigrants. The author even makes the absurd statement that the the goal of a society is to give more people the opportunity to be famous and stand out. Really? Is THAT is goal of a society - to make Kardashians of us all? And if more people are famous and stand out, then doesn't than mean no one is famous or standing out?

In an argument like this, the author never addresses the fact that the countries and geographic regions many of these people come from are in utter chaos and violence. Where are the Big Data numbers explaining the immigration policies of England and France and the escalating violence in those nations between cultures with a history of not being able to live together. A free-for-all and open immigration policy in any country only serves to migrate the violence of groups that already cannot live together.

Yes, this review has a conservative slant to it. Maybe one could say - If you are a liberal you will love this book and if you are a conservative you will get your shorts in a bunch. The data and implications in this book carry much potential and room for discussion - but in the end the left-sided jabs throw doubt into the ability of Stephens-Davidowitz to present untainted data. He speaks of reviewers throwing out his articles for publication and, after reading this book, I can understand why.

We all know that humans present a biased, unrealistic portrayal of themselves to others. But how to know what's really going on? Seth Stephens Davidowitz has found ingenious ways to answer these questions and shed light about private preferences, practices and beliefs about everything from sex, religion, abortion and politics. It strikes me as a successor work to Freakonomics.

This was an easy read, a very interesting read, and quite eye opening. It's a book I've quoted and referred to ever since I read it. This was never an area I was even remotely interested in, but I'm glad I gave it a try. I never recommend books, but this one was worth it.

I can't say enough good things about this book. There are a ton of important and really insightful findings in here, all packaged into stories that are entertaining and enlightening to read. I'm having trouble getting into the new book I'm reading now because it seems like a let down after reading Everybody Lies...definitely a must read.

Everybody Lies is a fantastic book for anyone interested in Big Data regardless of your experience in the field. The author is able to explain concepts people view as very complex as simple ideas with fantastic and humorous examples.

Just finished reading Everybody Lies --- great read! Highly recommended for those who enjoyed Freakonomics or Nate Silver. A number of good applications of Google Trends in understanding human behavior. Also has nicely described comments on the advantages and challenges of leveraging Big Data in general. Helpful ideas in understanding what happened in our most recent presidential election. Seems like there is a lot of untapped potential utilizing Google search data.

Humorous, insightful, and light-hearted. When tackling big issues, Seth makes them approachable and goes over the top to make them interesting.

Towards the end, he references Freakonomics as an inspiration for the book and his career, but to compare Everybody Lies to Freakonomics does a huge disservice to Everybody Lies.

I can't wait to read the sequel!

This is a fascinating book that brims with implications for public policy and decision makers. How can we best use big data to enhance public health? What can Google searches tell us about social issues? How might our fears, hopes, and curiousities inform politics? Stephens-Davidowitz's writing is engaging and friendly, making economics and data sets accessible to the average reader. Anyone who has ever typed a question into a search bar can pick this up and learn something interesting.

Awesome book covering big data analysis. Some of the research topics and insights you may not want to know, but this is a great overview into what big data is, why it's important, how we can use it to better our lives, and what we need to watch out for so we don't make mistakes. It also puts quantitative research behind some things you may have always expected to be true. Worth the read for sure.

I find the information discovered from people's internet searches fascinating. I love every part of the book that cover that. My reason for 3 stars is that, in my opinion, author implicitly has some unsubstantiated opinions - such as that Trump is racist or at least more so than Clinton. Perhaps there is a way to "prove" this true or false with data?

This book will appeal to those who like sensationalist literature. Too much about porn sites and sports. I can’t believe it won awards. While it could have offered valuable insights about social behavior, it missed the mark for me. And I’m not lying.

The major premise of the book, right there in the title, is that self-reported data are unreliable. (We already knew this.) Yet the author relies on just that sort of unvalidated data for many of his observations and conclusions. At one point, he tells us that most participants in an on-line hate group called StormFront are in their 20s. How does he know? By looking at their ages in their self-reported on-line profiles. Elsewhere he relies heavily on conclusion drawn from GoogleTrends that that source cannot substantiate.

FANTASTIC BOOK!

I can't give this book enough praise.
The wealth of knowledge I gained from this book is incredible. As a recent graduate, I love all the different avenues that data science is provided value. And Seth does a great job is describing unique questions and conundrums that data science has shed light on.

Do yourself self a favor and read this book!

Very engaging and entertaining presentation of important issues related to the flood of data that is surging about modern life. Many surprising findings, valuable analytical approaches and sensitivity to the ethical and human issues data science faces going forward.

Get this book. Get it for your family and friends (but be careful some topics are not suitable for children). Guaranteed you will have something to talk about at your next social gathering. It is witty and engaging for a wide range of audiences. You don't have to be a data scientist to like it, but if you are, there is something in it for you too. I couldn't put it down.

Great read for practically anyone - and especially those interested in data analytics, or those simply looking for more insight into how the digital world allows us to shine a light on various topics; racism, politics, sex. A fascinating look at human behavior cataloged through the footprints we leave on google, facebook and other data-rich websites. Highly recommend.

Very original, relevant content. As a data enthusiast, I appreciated the unique approach and interesting data filters the author applied. Some of the findings are fascinating. The ability to back check health symptoms that lead to life threatening diseases is VERY powerful.

This is a great book...it helps the reader look at things differently and show how data is increasingly being used in our everyday lives, even if we are not aware. I enjoy books like this one, so this one ranks right up there with Freakanomics. I highly recommend this book.

Certainly interesting with one of the best conclusions I've ever read. I did take issue with the many times the author makes a big claim and then doesn't back it up or site his specific source for that fact. Worth reading.

Fascinating overview of what can be learned from the many sources of data in our lives, and some reasonable concerns about potential abuse of said data. But overall a hopeful message, and yes, I read it to the end...

I picked up a copy of this based on the review in the Economist and read it in four days. It is both entertaining and insightful. A must-read for anyone who has an interest in applied social sciences.

SSD is on to something big, very big. It completely fulfilled my expectations, which were huge. I think calling it remarkable is understating the work here. Completely innovative and superbly written. Bates? Nobel? I really hope so. He deserves it.

Before anything, I would like to state that it is not a verified purchase because I did not purchase this from the store. This main issue with this book is that the author fails to take into account numerous other angles before making conclusions. To give just one example, he talks about the media at one point, and mentions the liberal media bias. After citing one study, he makes the claim that media is more liberal because those are the stories that the public wants. He completely disregards all external effects such as the long history of government meddling and near monopolies. In addition, this would have to only be applicable to the United States. In Europe, particularly the UK, the citizens are subjected to taxes to pay for the BBC. Lastly, this comes down to a "chicken or the egg scenario." Is the media more liberal because that's what the public wanted in the first place? Or is it because they have been liberal for so long that the public is used to it? There are various other issues in this book that are half-haphazardly covered, and it makes me weary on the issues that I enjoyed learning about. Is he right? Should I even trust him on these ostensibly mundane issues? I guess I would have to do my own research to see if his research is correct.

I finished this book this evening and it was great. From the beginning to the end it kept my attention even with a house full of kids and a wife constantly in my ear with honey do's. I GENUINELY cannot wait for EVERYBODY (STILL) LIES! Keep the content coming!

"In other words, people tend not to finish treatises by economists."

I've done my part to make sure it's known this book has been read the whole way through. Do the same, and you'll understand.

This is a terrific book which demonstrates the awesome opportunities of big data and the power of modern data science. As an economist I particularly liked how it emphasized the usefulness in the big data context of modern econometric methods used to analyze natural experiments. And I learned a lot about how innovative data science methods are transforming the way we measure economies, with e.g. Premise using photos taken in emerging economies to provide real time nowcasts of economic activity. This book makes you want to log on to Google Trends and to start exploring trends and patterns relevant to your field of interest. It is a must read, and as the author hoped has definitely superseded Freakonomics.

More of a high school senior year social studies 101 second semester text book than what I believe most people would consider a novel.
Some interesting extrapolations from Google and Microsoft internet search data, but really nothing earthshaking or really memorable here.

It's Really hard to read a book that starts off by calling you a racist for voting for Donald Trump. The author has interesting ideas but is a typical arrogant hard core leftist and I had a really hard time finishing the book. Next time don't inject your political views in the first chapter and alienate half the country.
Here is a paraphrase from his book: "Research says that people who grew up in an immigrant neighborhood are more successful when they grow up. Take that Donald Trump!" Are you writing a serious book, or are you an immature leftist who just graduated college? . Why can't you just do your "scientific research" (he LOVES calling himself a scientific researcher...) instead of turning everything political.
He also sounds like he wishes he was the next Malcom Gladwell...

What a book. One of the best I have read in years. Devoured this in 2 days. Really interesting facts, but a very natural and easy read. Would highly recommend, rekindled my love of mathematics and data!

The book does not so much tell us about how people really are as much as tell how the internet can be used to support personal beliefs and ideologies. The scenarios provided lessons on how to manipulate data. The author believes a proper conclusion sums up and set parameters for the next steps. He advises readers to follow what people do and not what they say. Throughout the author talks about what he has done or should do. The notes provide an interested look at how the he used data to support theorems but not why he selected specific propositions. It was interesting to read, but as the title says, it could all be a lie.

I was randomly chosen through a Goodreads Giveaway to receive this book free from the publisher. Although encouraged, I was under no obligation to write a review. The opinions I have expressed are my own.

Couldn't put it down. The book is full of insight into what people are thinking and not saying. And while there are some wild conclusions drawn from the data, Seth is cautious about jumping to them. I get the sense that while Seth is researching and writing, he's repeatedly asking himself "How could I be wrong here?" and exploring where that goes. He's often self-deprecating, and never forceful in his tone. I like that he admits and analyzes how much uncertainty is involved.

A political piece masquerading as a neutral study on big data. That abject marxists like the author of this book have their hands on the most important data sets for the most influential software platforms should be troubling to everyone.

He makes a lot of hay about racist Google searches and arrives at very dubious and self-satisfying conclusions that, since they came from the same parts of the country that voted for Donald Trump, this explains why he won since, see, Donald Trump is a racist. Never mind the most controversial things Trump said about race had to do with illegal immigration from Latin America, not black people, he still appeals to the same people looking for jokes about black people, because the author says so. Be quiet, the debate is over.

And ever mind that Trump polled better among black voters than any other Republican candidate, or that the voters in 2016 were not in the same position they were after 8 years of stagnant economic growth under Obama, or that Obama actually won some of those same places the author considers racist havens, they're all just chock full of closet racists waiting for someone to trigger them. It doesn't take long to discover the author has a very obvious bias or even a deliberate agenda. While the data itself can be fascinating, for a supposed scientist, the conclusions are as sophomoric and amateur as they are predictable from the leftist millennial author with Obama White House connections.

Given his shock at things it doesn't take a data scientist to figure out, the world view of the author is apparently limited to Silicon Valley/Washington DC bubble areas. "Why are some papers left leaning and others right leaning?" he ponders. While he's befuddled at all the data showing left leaning (and, much more rarely, conservative leaning) journalism, anyone who isn't a committed leftist would realize just from paying attention that whaddya know, the journalism profession attracts liberals. It's really a s simple as that. The only conservative-ish major televised news outlet is Fox, and that's only because Rupert Murdoch recognized a market for a different flavor of the standard liberal worldview, not because everyone who works at Fox is a die hard republican - hardly. Many of the broadcast babe over there come and go to different news organizations and are just as readily hired by left leaning broadcasters with hardly a fuss. It's a job, plain and simple, and like Hollywood, it attracts lefties.

Incidentally, it was around this part of the book I learned that the word "homosexual" is disparaging. Eh? Since when, and who says? I've got two gay relatives who never told me that. I'd better update my subscriptions...

Much of the book is fixated on lurid details about sex-related searches, and of course the author has ideas as to what steps to take next to capitalize on these results. For states with low tolerance for homosexuality, the search data suggested there were many closeted homosexuals, particularly among high school students since they are not in a position to move from the area voluntarily. He surmised that this means things should be done to bring them out of the closet and engage with their real desires. But then when he discovers that nationwide, women make up 25% of the searchers at PornHub and a disproportionate number of them search for pornography about rape (many more do this than any group of men), he backs off. He cautions that "well just because women may search for this we should not confuse fantasy with reality - this doesn't mean they want to be raped, etc."

Ok, that's understandable, but can't the same be said for the high school students - male and female - searching homosexual pornography? Why is it ok to encourage one line of behavior and not the other? Why is leading an underaged and confused high school student to a lifestyle they may not really want more acceptable than encouraging a woman to engage in rape fantasies? A grown woman is in a better decision to decide what kind of sex life she prefers - no matter how disturbing and harmful - than a minor with questions about homosexuality, but the author seems fixated and predatory in his desire to explore and encourage the latter. That he is working with activist groups to target areas of the country he thinks are not gay enough is a reminder of what kind of influence those who hold the keys to the data kingdom can have.

Given the petabytes upon petabytes of available data from Google and other sources, it came down to the author to decide which avenues to explore. He chose, principally, race and sex based themes for much of his work, with political influences running close behind. It wouldn't be as exciting or lucrative (politically or financially) to focus on brownie recipes or spotted owl mating habits, and he certainly wouldn't deliberately target members of his own socioeconomic and idealogical strata for examination. Whatever negatives he did find out about people like himself were likely unintended and, to him, surprising. I have to wonder what other unflattering information he found out about his fellow liberal bubble-dwellers that he did not share in the book.

If you're still interested and just want to cut to the chase, there's interviews of the author online where he basically tells the whole story for free without taking so much of your time to do it. Save your money.

This book is a coherent and thoughtful take on how big data will change and has already changed our world. It's packed with fascinating anecdotes, and the author's self-deprecating (Woody Allen-esque) sense of humor makes it so fun to read. A wonderful read!

Wanted to like this book. Big data and social research is very interesting to me when presented in an unbiased way. This author is liberal and bleeds through his conclusions and this book. Taking objective data and twisting it to conform to your ideology, makes this more liberal propaganda.

The content of this work is interesting (I think), but the presentation is not. Seth Stephens-Davidowitz suffers from an excess of youth and testosterone, and a lack of English gramma. He tells us on page 124 that he wanted to call this book ‘How Big in My Penis?’ That sums up Mr Stephens-Davidowitz in five words. He is also a ‘Never Trumper’, and never misses an opportunity to acquaint us with this riveting information. What this has to do with statistical analysis I never discovered.

The writer spends his time crunching unrelated data to arrive at interesting/valuable/useless (you choose), information. To do this he must spy on confidential information that unsuspecting individuals have fed into Google. He propounds that if you ask people delicate questions (mostly sexual), they will lie – hence the title.

Really? What an astounding revelation.

The general idea is, that if you feed enough seemingly unrelated data into a computer a trend will develop. The book then shares this information with the reader. It is apparent that Davidowitz is obsessed with sex, and I’m a little astonished that his book is available to the general public. I would have thought an Adult Book Shop would have been more appropriate.

While reading this work; if you can drag yourself away from the titillating intelligence that women like watching pornography showing other women doing things to each other, and men like even worse things, then some of the non-sexual data is revealing – but be prepared for a lot of Looney-Left bias.

I’m glad I read the book, and I recommend it to anyone who has normal blood pressure. Some of the information is revealing, and after all that is all Davidowitz claims for it. As the author matures I think his work will become valuable.

Sounded like it would be a interesting read. But his political views were strongly injected into this book. I learned in the introduction that Obama and his family were so amazing and that Trump promotes violence and has made racism more prevalent. These were his views, not proven facts. Hard to read a book and trust if the author will be nuetral or push his views when he pushes it so quickly at the introduction. I stopped reading it and will try to return it.

The prism the author views America through is so far left leaning it makes me question so many of his points. The book is a giant reminder that data may be neutral, but the way you present it is not. The title of the book is ironic. The smug arrogance of the the left continues.