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663 reviews for:
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us about Who We Really Are
Seth Stephens-Davidowitz
3.82 AVERAGE
663 reviews for:
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us about Who We Really Are
Seth Stephens-Davidowitz
3.82 AVERAGE
When I started this book, I felt like it was going to be a 3-star read. The first couple of chapters were full of clickbaity (I'm sure there's a real word for it but I like the sound of this) insights about the human psyche - sex, Islamophobia, racism, how to cheat on your taxes. It didn't seem to deep dive into the research or depict intellectual prowess. As I progressed with the book, however, I was more and more engaged. When I read the conclusion, the entire structure of the book made sense. It's a good, light read, and asks some important questions, while providing some interesting answers.
Data science is cool. I would never know what questions to ask, but it sure is interesting to read about!
Warning: white dude using the n-word (in reference to Google searches)
Warning: white dude using the n-word (in reference to Google searches)
informative
fast-paced
This book is really eye-opening on how you can use big data to understand social relations as they are. Main points: people lie everywhere except in their anonymous searches, and the analysis of those searches tell a pretty frightful tale of who "we" are; big data is really detailed data about lots of searches, allowing for much finer analysis by zooming in but also reducing privacy to nil; big data allows analysis of unlikely events by sheer size; and big data lets social scientists do real science, including formal and natural experiments through "A/B" testing (the term Google uses for automated designed experiments about people's behavior). I would love to see big data combined with modeling like Bayesian networks or agent-based simulation, the other main contributors to "real" social science (that is, combine theory with data through mathematical tools--like we do in physics).
Personally, I would have liked more depth on analysis techniques and perhaps some material on how to get data, analysis tools and techniques, and how machine learning can work with big data, but I'm a social scientist/engineer/geek and I don't take away stars for that :). Evidence: I will read Everybody (Still) Lies. And Seth should get a wife/life--now.
Revision 4/5/2020, in the middle of COVID-19 pandemic.
See Seth's article in NYT:
https://www.nytimes.com/2020/04/05/opinion/coronavirus-google-searches.html?action=click&module=Opinion&pgtype=Homepage
I changed the rating to 3 stars because I think now that the data analysis comment above should be taken much more seriously than I took it at the time. Reading this 4/5/2020 article really opened my eyes to the complete invalidity of "big data" analysis in the absence of serious data analysis techniques. The tip of this particular iceberg comes down to the difference between correlation and causation, but if you read reported data analysis, especially in the media, and the analysis is both self-contradictory and full of basic modeling and analysis errors, you begin to realize that so-called big data is no more than hand-waving. Without an underlying theory or causal model that informs the analysis, you have no idea what produced the outward form of the data.
In summary: Everybody Lies, indeed, especially with big data and statistics.
Personally, I would have liked more depth on analysis techniques and perhaps some material on how to get data, analysis tools and techniques, and how machine learning can work with big data, but I'm a social scientist/engineer/geek and I don't take away stars for that :). Evidence: I will read Everybody (Still) Lies. And Seth should get a wife/life--now.
Revision 4/5/2020, in the middle of COVID-19 pandemic.
See Seth's article in NYT:
https://www.nytimes.com/2020/04/05/opinion/coronavirus-google-searches.html?action=click&module=Opinion&pgtype=Homepage
I changed the rating to 3 stars because I think now that the data analysis comment above should be taken much more seriously than I took it at the time. Reading this 4/5/2020 article really opened my eyes to the complete invalidity of "big data" analysis in the absence of serious data analysis techniques. The tip of this particular iceberg comes down to the difference between correlation and causation, but if you read reported data analysis, especially in the media, and the analysis is both self-contradictory and full of basic modeling and analysis errors, you begin to realize that so-called big data is no more than hand-waving. Without an underlying theory or causal model that informs the analysis, you have no idea what produced the outward form of the data.
In summary: Everybody Lies, indeed, especially with big data and statistics.
I don't particularly rate this book. It read like an overly long blogpost, with jaunty, clickbaity language to match. In fact, I think some of the studies in this book would have been better written up as blog articles, making use of multimedia to tell the story. Stephens-Davidowitz does spend a little bit of time discussing some of the issues with Big Data (e.g. the curse of dimensionality), but this cursory and comes too late in the book after he's tried to wow the reader with his attempt at Freakonomics.
Full of interesting and surprising insights. The writing was a tad dry but still engaging. A great follow up to other books like Freakonomics and Outliers.
I nerded out on this book, big time. The only reason it took more than two days to finish was because of a four day field trip to Washington DC with my students.
Because of said field trip, I did manage to forget significant portions of the first 60% of the book that I read before the field trip although I do remember referencing something I read in the book in conversation with a few of my students.
As a self-proclaimed data nerd, I rapidly consumed this book. I loved nearly all of the examples he gave and the pithy conclusions he drew from his various examples.
The primary reason I gave this book four stars instead of five is because his self-deprecating comments, while endearing at first, became almost overwhelming in the conclusion. Well, that and the fact that after less than a week, I remember few specifics.
I do, however, strongly recommend this book.
Because of said field trip, I did manage to forget significant portions of the first 60% of the book that I read before the field trip although I do remember referencing something I read in the book in conversation with a few of my students.
As a self-proclaimed data nerd, I rapidly consumed this book. I loved nearly all of the examples he gave and the pithy conclusions he drew from his various examples.
The primary reason I gave this book four stars instead of five is because his self-deprecating comments, while endearing at first, became almost overwhelming in the conclusion. Well, that and the fact that after less than a week, I remember few specifics.
I do, however, strongly recommend this book.
A book about big data from sources like Google, Microsoft, Facebook, etc., and the ways these new data sources can support current understandings, or completely up-end them.
Super interesting. The author does a good job showing the potential and pitfalls in big data. It's exciting to think about for curious people as a huge bulk of information that we're able to zoom in and try to answer questions like does showing violent movies increase crime on the streets ... or how do Americans hide their racism and where do they live (spoiler alert: if you think it's primarily in the American South, you'd be wrong)?
Since I was listening to the audiobook version, I missed out on seeing the charts and images being described, but it was easy enough to follow.
Super interesting. The author does a good job showing the potential and pitfalls in big data. It's exciting to think about for curious people as a huge bulk of information that we're able to zoom in and try to answer questions like does showing violent movies increase crime on the streets ... or how do Americans hide their racism and where do they live (spoiler alert: if you think it's primarily in the American South, you'd be wrong)?
Since I was listening to the audiobook version, I missed out on seeing the charts and images being described, but it was easy enough to follow.
The book debunks clusters of everyday fallacies.
I enjoy being wrong. Whenever there was a fallacy the author debunked, it brought me joy.
A lot of this gave me a chuckle but none of it really moved me or had me thinking too deeply.
I liked how the author outlines big data and points to its flaws and limitations.
It is a spiritual successor of sorts to Freakonomics, some of the claims there were debunked so in the back of my mind I wondered how much of the statements made were true so I took parts of this with a grain of salt.
Honestly I have a hard time understanding others. Often I just want to understand how they think. Hence why I read this book. The author's telling of google search, pornhub and other searches were enlightening. People have no filter on google. But someone is always watching.
The morality of using big data is something I struggle with.
Having Netflix give you suggestions you never thought of based on another person who watches the same shows as you do is harmless.
But when serious topics like life insurance or gambling can be essentially gamed by corporations to exploit the most amount of profit I worry about free will.
Honestly this reminds me of the old sci fi novel Foundation by Isaac Asimov in 1951.
A statistician named Hari Seldon predicated the future of humanity for millenniums based on the big data of his time. Like big data of today Seldon could not predict the outcomes of the individual but instead by humanity as a whole. Having people who can see our future, know what actions we take seems cool yet at the same time is tremendously scary to me.
I enjoy being wrong. Whenever there was a fallacy the author debunked, it brought me joy.
A lot of this gave me a chuckle but none of it really moved me or had me thinking too deeply.
I liked how the author outlines big data and points to its flaws and limitations.
It is a spiritual successor of sorts to Freakonomics, some of the claims there were debunked so in the back of my mind I wondered how much of the statements made were true so I took parts of this with a grain of salt.
Honestly I have a hard time understanding others. Often I just want to understand how they think. Hence why I read this book. The author's telling of google search, pornhub and other searches were enlightening. People have no filter on google. But someone is always watching.
The morality of using big data is something I struggle with.
Having Netflix give you suggestions you never thought of based on another person who watches the same shows as you do is harmless.
But when serious topics like life insurance or gambling can be essentially gamed by corporations to exploit the most amount of profit I worry about free will.
Honestly this reminds me of the old sci fi novel Foundation by Isaac Asimov in 1951.
A statistician named Hari Seldon predicated the future of humanity for millenniums based on the big data of his time. Like big data of today Seldon could not predict the outcomes of the individual but instead by humanity as a whole. Having people who can see our future, know what actions we take seems cool yet at the same time is tremendously scary to me.