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665 reviews for:
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
Steven Pinker, Seth Stephens-Davidowitz
3.82 AVERAGE
665 reviews for:
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
Steven Pinker, Seth Stephens-Davidowitz
3.82 AVERAGE
adventurous
funny
informative
reflective
fast-paced
Overview:
Human thoughts are very complex, making many different ways to access them subject to a variety of limitations. Big data via the internet is very revealing about people. What people search for is information that can reveal what they are thinking about. Providing a different way to learn about people. There are four ways that big data adds value to research which are 1) new types of data, 2) honest data, 3) zoom on small subsets, and 4) generate causal experiments. The internet is a primary source of data that uses messy traces, unlike traditional sources of data such as surveys and questionnaires which are very neat. Big data has many limitations as well, but it can help in a variety of situations. A problem with big data is that as it enables better predictions about human behavior, the information can be misused by corporations and governments leading to various forms of nefarious discrimination activities.
Human thoughts are very complex, making many different ways to access them subject to a variety of limitations. Big data via the internet is very revealing about people. What people search for is information that can reveal what they are thinking about. Providing a different way to learn about people. There are four ways that big data adds value to research which are 1) new types of data, 2) honest data, 3) zoom on small subsets, and 4) generate causal experiments. The internet is a primary source of data that uses messy traces, unlike traditional sources of data such as surveys and questionnaires which are very neat. Big data has many limitations as well, but it can help in a variety of situations. A problem with big data is that as it enables better predictions about human behavior, the information can be misused by corporations and governments leading to various forms of nefarious discrimination activities.
People tend to be way more honest in their search terms rather than what they claim about themselves. Depending on the context, they can be honest because of potential consequences, or because there are no consequences. Surveys are usually anonymous, but people still lie on them because they want to look good. The desirability bias causes people to lie about who they are relative to who they want to be. Impersonal data tends to be more honest. People will confess when they are alone rather than in the presence of others. Facilitates knowing what people do rather than say they do.
Big data facilitates better understanding of the topics which can lead to better resolution methods. Big data reveals that individuals are not alone in their insecurities and embarrassing behavior. Making overt covert suffering. Google data can highlight many vulnerable people, as they might not want to report their trauma to official sources.
Offline experiments are time consuming and costly. The digital space enables cheap and fast randomized experiments. Gaps in understanding can be filled by testing. Gaps always exist.
Big data has limitations. The numbers measure what can gathered, not necessarily what is wanted or important. Models created from the data does not indicate the reason why the model works. Knowing why models work may not be that important. But with this limitation, there is no indication of insights that can be gained and ways to improve understanding of the topics. There are data sources in which do facilitate lying rather than honesty. When there is no incentive to tell the truth, people make themselves appear better. Online presence is not always anonymous, and can cater to an audience.
Caveats?
The book is well written with plenty of examples and provides a general understanding of the power and complexity of big data. There are many topics in this book which are very sensitive, as in very private and personal. As such, the book may not be appropriate for minors.
The book is well written with plenty of examples and provides a general understanding of the power and complexity of big data. There are many topics in this book which are very sensitive, as in very private and personal. As such, the book may not be appropriate for minors.
Although big data does open up more opportunities to consider how people think, what matters is how the data is interpreted. There are a variety of interpretations of the data, of which there can be many misleading interpretations. Big data does offer lots to think about, but not how to think about what it brings up.
Content Warnings
Moderate: Sexual content
There are many topics in this book which are very sensitive, as in very private and personal. As such, the book may not be appropriate for minors.
I loved this book. It is definitely in my top 5 books of 2019, I cannot recommend it enough. I read some of the other goodread reviews and no one else seemed to have as good a time as me, so take this review with a grain of salt i guess.
Basically this book is giving you tangible data about how google probably knows you better than most of your friends and family. I don't really want to spoil much as I truly recommend the read! The data he provided was super interesting to me and proves how much you can gleam from internet searches. Topics covered range from voter turnout, election results, sexual preferences, methods of speech, sports and some other topics I'm sure I'm forgetting.
As always with data scientist, they don't seem to worry too much about the ethics of what they're doing, the author covered ethical questions in a mere 10 pages but I get that that's not the point he's trying to make so I'll give him a pass. The author is successfully explains how the social sciences will be turning more and more to data rather than surveys or small experiments when you can get access to pools and pools of data from Facebook, Google and Pornhub. He goes into what makes good research a bit (what to do with huge amounts of data etc) - if that's what you're after its quite sparse in terms of how to actually conduct good research but the results he shared were fascinating.
Also it's an easy read! Read it in 4 days, it's the holidays what do you have to lose!
PS. Appaz lots of people don't finish non fiction books?? By his standard fewer than 10% make it to reading the conclusion (he was looking at economics books but still) ?? Wild times man
A somewhat breezy, entertaining collection of stories and speculations regarding unexpected statistical correlations. It reminds me of and occasionally refers to Freakonomics. Stephens-Davidowitz's joy in his work comes across. I appreciate books like this that provide enough information on core concepts and methodology to give a sense of the rigors and significance of a practice but without a barrage of unexplained jargon and tedious details. (Those are what endnotes are for.) I also appreciate how data science explanations for non-specialists can highlight the impacts of innate and learned cognitive biases on rationality.
informative
fast-paced
It was interesting to hear the examples but nothing very new for me. At some moments the author was a bit off in the jokes and this bothered me but not to great extent.
I picked up this book to get the interesting facts that Stephens-Davidowitz learned from his analyses of this revealing dataset. That said, there is also plenty of basic introduction to data collection and research methodology, which might be a bit tedious for anyone who is already familiar with this material. However, I appreciated the attention to basics when it came to statistical analysis, an area where I don’t have the same background knowledge or experience. The author also spends a good bit of time trying to convince skeptics on one side that big data is useful, and on the other side, warning evangelists of the limitations. A big dataset can actually be an encumbrance if you don’t know what questions to ask of it. However, I sometimes took issue with the way the author tried to present information in an accessible way. Comparing a large dataset to your Grandma’s lifetime of collected wisdom is more harmful than helpful because only one of those things is based on verifiable numbers rather than impressions. read more
informative
medium-paced
Interesting. I think sometimes he alludes to causation when it’s just correlation (and then discusses how that’s bad), but the correlations are interesting. I like the idea of having another data set.
Interesting facts, but a little scattered
I first heard about Everybody Lies from the podcast Fresh Air. I was deeply interested in the concept of a data scientist using the data from Google searches to generate insights about human behavior.
It was an interesting concept, but doesn't quite stick the landing in terms of execution.
As I progressed through the book, one thing that I kept trying to figure out was if the intention of this book was to unearth humanity's deep dark secrets or a guide to understanding and demystifying data science. The result was somewhere in between.
Don't get me wrong, I enjoyed learning things like how American Pharaoh was destined to be a Triple Crown winner or whether what college you go to really does correlate with greater success in the long-term.
But the author just sets these up as examples of a concept of data science. I felt like the back and forth did not give the insights or the data science concepts full justice.
The book starts off a little scatterbrained in the first few chapters but really begins to pick its stride in the later chapters. I genuinely feel like I have a fundamental understanding of the methodologies of data science as well as a few cool facts to drop at my next cocktail party. My advice to anyone reading is to stick with the book because as unfocused as it may seem, you will still learn something.
I first heard about Everybody Lies from the podcast Fresh Air. I was deeply interested in the concept of a data scientist using the data from Google searches to generate insights about human behavior.
It was an interesting concept, but doesn't quite stick the landing in terms of execution.
As I progressed through the book, one thing that I kept trying to figure out was if the intention of this book was to unearth humanity's deep dark secrets or a guide to understanding and demystifying data science. The result was somewhere in between.
Don't get me wrong, I enjoyed learning things like how American Pharaoh was destined to be a Triple Crown winner or whether what college you go to really does correlate with greater success in the long-term.
But the author just sets these up as examples of a concept of data science. I felt like the back and forth did not give the insights or the data science concepts full justice.
The book starts off a little scatterbrained in the first few chapters but really begins to pick its stride in the later chapters. I genuinely feel like I have a fundamental understanding of the methodologies of data science as well as a few cool facts to drop at my next cocktail party. My advice to anyone reading is to stick with the book because as unfocused as it may seem, you will still learn something.
A seminal book
I believe this book will have a similar impact as the Freakomics - which this author admires. I am sure google search and other data from internet usage is going to have profound affect on the field of social Sciences. Great read.
I believe this book will have a similar impact as the Freakomics - which this author admires. I am sure google search and other data from internet usage is going to have profound affect on the field of social Sciences. Great read.
4.4
Excellent read. It appears this may be the welcome bolster that human sciences need - data, specifically- data that largely subverts social desirability bias based on the premise that we lie to everyone except, perhaps, our internet explorers.
There’s all sorts of wild data in this book and it’s quite fascinating to apply the principals of data analysis to qualitative questions. Although, as @cups has been exploring, there must be consideration for the implicit biases built into analytical systems and I think that could be relevant here. Also, mentioned are the ethical implications that might arise from implementations based on this kind of data mining.
I was reminded of ‘Invisible Women’ whilst reading- these analysis approaches will no doubt be welcome in the context of plugging long neglected data gaps.
Super interesting all round but weak ending imo. Unsure if purposeful following mention of few bothering economics books to the end but, even so, it was weak. Some ppl are saying it’s too like Freakonomics. Not read that and THUS can’t comment.
Excellent read. It appears this may be the welcome bolster that human sciences need - data, specifically- data that largely subverts social desirability bias based on the premise that we lie to everyone except, perhaps, our internet explorers.
There’s all sorts of wild data in this book and it’s quite fascinating to apply the principals of data analysis to qualitative questions. Although, as @cups has been exploring, there must be consideration for the implicit biases built into analytical systems and I think that could be relevant here. Also, mentioned are the ethical implications that might arise from implementations based on this kind of data mining.
I was reminded of ‘Invisible Women’ whilst reading- these analysis approaches will no doubt be welcome in the context of plugging long neglected data gaps.
Super interesting all round but weak ending imo. Unsure if purposeful following mention of few bothering economics books to the end but, even so, it was weak. Some ppl are saying it’s too like Freakonomics. Not read that and THUS can’t comment.
You can clearly see the impact of Freakonomics on this book. But all in all, a pretty interesting take on how to see the world through Data Analytics.