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

Smart and entertaining. Good and simple book about big data analysis with real cases

This book is in that particular genre where the author tries to make his or her area of expertise (often physics for some reason, though clearly not in this case) palatable and accessible to the "common (wo)man." These types of books fail when the author doesn't dumb it down enough or dumbs it down too much. Stephens-Davidowitz's area is economics/social science by way of Big Data, and he dumbs it down just the right amount.At the beginning of the book, my inner skeptic was anxiously asking about correlation vs causation and how people can know they're asking the right questions of the right data. By the end of the book, Stephens-Davidowitz had satisfactorily addressed most of my initial concerns and provided some insight into data science, social science, and some aspects of human nature along the way. Plus, the book made me laugh (well, chuckle) out loud more than a few times, which means I was pretty engaged and is not bad for a book about data science.Some notes: - The subtitle ("Big Data, new data, and what the internet can tell us about who we really are") is slightly misleading. While much of the book does rely on search queries (predominately Google) and Twitter and Facebook updates, plenty of the analysis and studies rely on non-internet data sources. Stephens-Davidowitz is clearly excited about all of the new ways to use all of the new internet data, but the overall focus of the book is on Big Data of all kinds and its powers and drawbacks.- Some chapters illustrate the fact that people admit things on the internet they would not admit elsewhere. Issues addressed include porn preferences and racism, both discussed in detail, and child abuse, discussed in less detail. Although the possible conclusions range from unsavory to downright depressing, the topics are relevant to addressing the book's points about data and social science; however, worth noting because some readers will be sensitive to these topics.(Thank you, Dey Street Books and GoodReads for the ARC.)

Fascinating perspective and an interesting interpretation of the data. The narrative was engaging and rather fun to read.

Couldn't get past the beginning of the book because of the obvious personal & political bias. I want facts & evidence. Not opinion.

This book is waste of time unless you have too much time on your shoulder

This book was very insightful, could’ve done without politics

Great read. Funny and insightful. Worth picking this book up. Yes I finished it even though big data says I wouldn't.

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I love it, funny and easy to read with lots of insights.

To open your mind, about surround is about data, using big data to make your life easier, your job effectively, you also use ethic to control use the big data analysis

Terrific book, absolutely loved it, I finished the audiobook in one day.

Must read book for those who want to understand the big data in a big way... Thanks Seth for writing such a wonderful book.

The upshot of this book is not that big data is the holy grail. Rather, the recurring theme in all of Stephens-Davidowitz's interesting examples is just that most self-reporting is awful.

I'm still skeptical about the big data revolution--and this book doesn't really focus on implicit bias in analysis of large data sets--but the conventional research methods of social sciences are amusingly torn to pieces (much like advertising ROI was absolutely shredded in the digital age where measurement was no longer entirely by gut).

When you read an original book, in full, you expect to get the full benefit of the author’s thinking process and have their ideas spelled out in detail. When you are just reading a summary, then you can expect something less. And, in this case, my review is of the Blinkist summary. When I’ve had the chance to check a Blinkist summary against the original book, the summary has come out pretty well. And life is short; so I’ve been reading a lot more of the summaries recently. If they really grab me, I will seek out the original. Meantime, here are a few snippets from the summary that caught my attention:
Big data is data for which computational power is required to recognize patterns.
utilizing gathered data correctly is essential to refining one’s worldview......It helps us identify more precise patterns and predictions than personal experience alone ever could.....Though a gut feeling may get us far, data refine even the most intuitive person’s perspective.
What set Google apart was that the collected data could be used efficiently.......Brin and Page’s algorithm [for Google] worked differently. They figured out that a website was likely more relevant to someone if it had more links from other sites that took a user to it....Google engineer Jeremy Ginsberg did. He showed that flu-related Google searches, such as “flu symptoms,” indicate of the spread of influenza, and can be used to track the spread of the disease across geographical areas and over time.
In a survey, two percent admitted they had graduated with a GPA lower than 2.5 on a four-point scale. However, according to official records, the number was much higher, at 11 percent.......It is a universal truth about surveying: people lie.
This behaviour of giving answers that make us look better is called social desirability bias.
Why big data is so powerful: it doesn’t lie. Because it's collected through unfiltered online behaviour, it will always reveal the truth......But big data, it isn’t flawless. Its biggest limitation becomes patently clear in datasets with many variables:.......it’s difficult to extract reliable answers because the number of variables obscures possible findings.
There are so many variables that patterns can occur randomly......For instance, Facebook can easily measure clicks and likes using big data. But doing so would tell the company nothing about people’s experience with the site......In circumstances like these, small data is essential. Facebook gathers this sort of data through other methods,
Every month, there are 3.5 million suicide-related Google searches in the United States. By contrast, the number of suicides in the country is lower than 4,000 a month.
Should governments even be allowed to possess and use search data pertaining to individuals?.......But more and more evidence points to a correlation between online searches and subsequent action.......Suicide-related Google searches are significantly correlated with actual suicide rates. But that correlation was only valid at the state level.
The key message in this book: People rarely fill out surveys honestly, which skews our understanding of the world. But with the rise of big data–that is, the collection of incredibly large amounts of data from, for example, Google searches–we are able to spot patterns in human behaviour and identify preferences that we never knew about before.
Actionable advice: Don’t fret if you have kinky sexual fantasies. You’re not alone! Although you probably won’t get everyone to admit to their fetishes, this may be just because some individuals worry they’ll be socially excluded. So, if you dare, speak up about your true preferences! You’re likely to get some weird looks, but as big data reveals, there’s almost certainly someone out there like you. Instead of hiding it, you can make all the kinky and strange stuff you normally type into Google a topic of conversation. Maybe then you can start normalizing some of the unspoken aspects of human behaviour.
My take on the book? Overall, I liked it. Learned a few new things. Learned a bit more about big data and the Google algorithm (that I should have known, but didn’t). I know my son in law’s company is making great use of big data and spending patterns. (And I suspect my Bank has been using it on me as well)......and there are some rather scary aspects to that. Especially the bias in algorithms ....which are fine unless you are the person who is always pulled aside at an airport because of some kink in the algorithm. And I was aware that surveys were always unreliable because I’ve used them in the past and they have been very poor predictors of what people actually did compared with what they said they WOULD do. Four stars from me.

While this book is a somewhat sort and easy read, it is nevertheless an important read. Big data is everywhere now, and many of the details of our lives are now recorded in databases. [b:Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are|28512671|Everybody Lies Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are|Seth Stephens-Davidowitz|https://images.gr-assets.com/books/1489069766s/28512671.jpg|48667223] explores the many uses of this data to learn what's really going on (or not) in our homes and bedrooms. It illuminates the extent that everyone lies to each other and themselves, by showing that our responses to surveys and other voluntary studies are often false. Some of these results are depressing, but all are enlightening. The book also explores the way that big data may be abused against us, but provides little hope for any way of preventing this abuse. A good, important, and timely book.

(Note: This book was provided to me by the publisher via a Goodreads giveaway.)

A light read regarding big data. He covers the positive uses and pitfalls of analyzing big data. The research does raise questions about the subjects covered. I just wish more methodology in the research used was shared.

An interesting book to have read after taking a social statistics class this last semester. I don’t agree with all his conclusions about the future of research and I would like to see additional evidence for some of his research but overall an interesting book.

Interesting book, but not that engaging.

Fascinating and also a little horrifying. Important read to understand how data is used to influence behaviors. I appreciated that Stephen-Davidowitz acknowledged the myriad ethical implications to consider when using and collecting data. If anything, this book reaffirmed my position that STEM careers cannot exist in a vacuum--we need the humanities alongside STEM to remind us that while data might help us make sense of our world, we aren't robots. Nuance and ethics are still important to our survival as a species.

Jumping to conclusions: the book. Chapters could be sold as clickbait articles.

A little disjointed, some of the humor falls flat. Worth reading for the tidbits, some glimpses into Google and Facebook's inner workings. Big Data is the future, obviously.

First half was very interesting content but was marred by the author's political views showing up in tacky ways. The second half he seemed to run out of content and spent time talking about himself too much and lecturing on why he and his work are so important. Let the content speak for itself. All in all, a good read though the author's age and immaturity do shine through and weaken this work.

An ongoing problem in research in psychology, political polling, and many other areas that rely on asking people questions about their views, activities, and experiences, is that people lie. Sometimes because the topic is a sensitive one, sometimes because they don't like pollsters, sometimes simply as a joke. Whatever the reason, a significant percentage of the people responding to any survey, will lie, and undermine the value of the data you think you're gathering.

This audiobook is about what you can find when you look at the the sources of data where people don't lie, because it would defeat their purpose rather than yours.

Google and other search engines are major sources of that "honest data." When people are looking for information, whether they're looking for help with their depression, or for racist jokes, they won't get what they want if they don't say what they want clearly enough for Google or Bing or DuckDuckGo to find it. Even on less charged or sensitive subjects, though, the search engines get far more data on any given topic than any manageable survey could retrieve in a workable timeframe.

And that data can tell us important things, trivial things, and things we might prefer not to know, such as where those searches for racist jokes are coming from, and what they tell us about who voted against Barack Obama, and why. Or where specific public needs aren't being met. It can tell us what are the symptoms people have when they are in the very early stages of pancreatic cancer, early enough that it might still be beatable.

To be clear, Stephens-Davidowitz looks at other sources of "big data" too, not just the search engines by any means, He also looks seriously at both the good and the bad that intelligent use of big data can offer; we want the government to be able to respond to public needs, for instance, but we don't want the Minority Report vision of a future with a Bureau of Pre-Crime, arresting you before you've even decided to do anything criminal.

Mostly it's lively and entertaining, as well as thought-provoking. Unfortunately for me, I was listening to it on Election Day and the couple of days following, when we didn't know what the outcome would be, and the section talking about the 2016 election was very hard on my nerves, and I nearly stopped listening. But that's my sensitivities and the timing, and I expect that section won't have that effect on most listeners.

I did find the conclusion to be unintentionally funny, as he went on and on about the importance of a properly strong conclusion not being verbose... But I forgive him for that. Recommended.

I bought this audiobook.

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.

Part of what for my interest was the answers the author got for some of life’s persistent questions. He concludes by suggesting big data will place the soft sciences on more solid footing.
I found myself wondering how he got access to the data and how google keeps track of so many questions I think of as unusual. I guess that odd part of Google’s business.

The author looks at ways big data can be used.

In Everybody Lies, Seth Stephens-Davidowitz explores the idea behind social desirability bias and how internet searches are helping Big Data paint a clearer picture about society. In short:

Many people under-report embarrassing behaviors and thoughts on surveys. They want to look good, even though most surveys are anonymous.

Stephens-Davidowitz posits that while people may lie to anonymous surveys they tend to type their true feelings and intentions into Google searches. It is this vast sum of new data that will allow researchers to make better predictions and offers brand new tools to allow insight all aspects of human behavior that direct questioning never could. It's a fascinating idea and the book provides plenty of food for thought.

The new age of Big Data is starting to show how wrong many of our assumptions about society are. How Google searches predicted Donald Trump's victory to common body anxieties to why people root for specific sports teams to the value of attending an elite high school to zooming in on health data and how it could change the way we receive care. It's eerie and a bit creepy when you stop and think about what people type into an internet search box, how much of that data is being captured and just what that data is starting to say about society. On the flip side, the author notes that Big Data has many pitfalls and it's a fairly new science that is still in its infancy.

Hard to tell if the author does good science because he does such uninteresting writing. I can imagine another book that could be so pleased with itself and, at the same time, so calculatingly composed to fulfill a contractual obligation. There is very little that is remarkable here, except for perhaps the author's obsession with porn. But even those endlessly prurient sections felt like someone trying to get more clicks for a lightweight article rather than actually contributing to the overarching theory of a book.

This was weak, sub-Gladwell, smug pop science. Not recommended.

The biggest appeal of books like these for me is finding out all the things that I thought I knew were false. The author shares a great sample of interesting things that illustrate the ways really big data groups can teach us things about ourselves that we didn't know or didn't want to know. This work did make me wonder if I should get off the internet and stay off!

Jam-packed with "who'd have thought it?" insights based on his professional data analysis skills, and reams of data, mostly Google searches. A wowser on nearly every page, many which you can't resist sharing. What's the magic age for a person to be for his team's World Series win to make him a lifelong fan? How could you have predicted where Trump would win based on offensive Google searches? Like Freakonomics meets Malcolm Gladwell. Fun!