Everybody Lies audiobook

…people’s search for information is, in itself, information. When and where they search for facts, quotes, jokes, places, persons, things, or help, it turns out, can tell us a lot more about what they really think, really desire, really fear, and really do than anyone might have guessed. This is especially true since people sometimes don’t so much query Google as confide in it: “I hate my boss.” “I am drunk.” “My dad hit me.” There’s lies, damned lies and then there are statistics. One must wonder. Do the lies get bigger as the datasets grow? Seth Stephens-Davidowitz posits that the availability of vast sums of new data not only allows researchers to make better predictions, but offers them never-before-available tools that can offer insight that direct questioning never could.We have seen steps up of this type before. Malcolm Gladwell has made a career of such, with Blink, Outliers, and The Tipping Point. Freakonomics is the one I would expect most folks would know. Nate Silver put his data expertise into The Signal and the Noise. All these looks at data and how we interpret it rely on the analyst, regardless, pretty much, of the data. While the same might be true of Stephens-Davidowitz’s approach, he focuses on the availability of materials that have not been there in the past. The smarts that must be applied to get the most interesting results can now be applied to new oceans of data. It is more possible than it has ever been to draw inferences and actually test them out. In addition to the volume of data that is now available, there is the sort. The author looks at Google and FB data for evidence of underlying realities. Surveys can sometimes offer inaccurate outcomes, when the people being queried do not provide honest answers. Are you a racist? Yes/No. But one can look at what people enter into Google to get a sense of possible racism by geographic area. The everyday act of typing a word or phrase into a compact rectangular white box leaves a small trace of truth that, when multiplied by millions, eventually reveals profound realities. Looking for queries on jokes involving the N-Word, for example, turns out to yield a telling portrait of anti-black sentiment, which also correlates with lower black life expectancy. (And pro-Trump vote totals) We are treated to looks into a variety of research subjects, from picking the ponies, to seeing what really interests/concerns people sexually, looking for patterns of child abuse, selecting the best wine, using the texts of a vast number of books and movie scripts to come up with six simple plot structures.I thought the most interesting piece was on the use of associations, and provoking curiosity, rather than relying on overt statements to influence how people feel about a different group of people. Another was on using a data comparison of one’s (anonymous) medical information to others who share many characteristics to improve medical diagnoses.There are some areas in which it was not entirely persuasive that the methodology in question was tracking what was claimed. SS-D sees in searches of Pornhub, for example, what people really want and really do, not what they say they want and say they do. Really? I expect that what people check out on-line does not necessarily track with what might be of interest in real life. It would be like someone with an interest in mysteries being thought to have homicidal tendencies after searching for a variety of homicide related titles. Should a writer doing research into a dark subject like child pornography, human trafficking or cannibalism expect the heavy knock of the police on his/her door? Where is the line between an academic or titillation search and one made for planning?SS-D makes a point about there being a significant difference between searches that offer projections for groups or areas, and their inapplicability for predicting individual behavior, although that will not necessarily remain the case. In baseball, for example, the explosion of available information may very well be applied to specific players to diagnose and even correct flaws in technique, or recognize patterns that might expose underlying medical issues, or predict their arrival. The Big Data related here is much more macro, looking at group proclivities. Useful for spotting trends, measuring public sentiment, but in more detail than has been heretofore possible.And of course there is the impact of dark players. Those with the resources and motivation could manipulate the Big Data produced by Google and Facebook. Such players would not necessarily be limited to Russian cyber-spies and pranksters, but corporate and ideological players as well, like Robert Mercer. There could have been a bit more in here on those concerns.The book offers plenty of anecdotal bits that could have been lifted from any of the other data books noted at the top of this review. What one needs, ultimately is smart, insightful analysis. Having all the data in the world (that means you, NSA) is merely a burden unless there is someone insightful enough to figure out the right questions to ask, and how to ask them.SS-D notes several Google (Trends, Ngrams, Correlate) services that might be familiar to folks doing actual research, but which were news to me. It might be useful to check out some of these, maybe even come up with meaningful queries to shed light on pressing, or even completely frivolous questions.Not all problems can be solved, or even examined by the addition of ever more data. Sometimes, many times, the information that is available is perfectly sufficient to the task, but other factors prevent the joining together of its various pieces to create a meaningful whole. The now classic example is from 9/11, when an absence of coordination between the CIA and FBI resulted in suicide bombers who could have been foiled succeeding in their mission. Politics and the culture of nations and organizations figure into how data is usedSo if everybody lies, is Seth Stephens-Davidowitz telling us the truth? I am sure there is a query one could construct that would look at diverse data sources, pull them all together and give us a fuller picture, but for now, we will have to make do with reading his book and articles, checking out his videos, applying the analytical tools already incorporated into our brains, and seeing if there is enough information there with which to come to a well-grounded conclusion. And that’s no lie. Review first posted – May 5, 2017Publication date – May 9, 2017=============================EXTRA STUFFLinks to the author’s personal, Twitter, and FB pagesVIDEOS – SS-D speaking----- Stanford Seminar - Insights with New Data: Using Google Search Data-----Google Sex with Seth Stephens-Davidowitz - Arts & Ideas at the JCCSF----- Big Data and the Social Sciences - The Julis-Rabinowitz Center for Public Policy and FinanceThe June 2017 National Geographic cover story has particular relevance to the treatment of actual truth in today's political environment. It is illuminating, if not exactly uplifting. - Why We Lie: The Science Behind Our Deceptive Ways - By Yudhijit BhattacharjeeJuly 12, 2017 - Washington Post - one of the very serious applications of big data - The investigation goes digital: Did someone point Russia to specific online targets? - by Philip BumpJuly 15, 2017 - One of the ways big data gets compromised is via automated dishonesty - Please Prove You’re Not a Robot by Tim Wu - Thanks to Henry B for letting us know about the article

This is an engaging book about how big data can be used to improve our understanding of human behavior, thinking, emotions, and preference. The basic idea is that if you ask people about their behavior or their preferences in surveys, even anonymous surveys, they will often lie. People do not like to admit to low-brow preferences; racists do not want to admit to their prejudices, most people who watch pornography do not want to admit to it, and even voting is often misrepresented; some people who voted for Trump would not admit to it. But, by analyzing immense datasets from Google, public archives, social media, and the like, Seth Stephens-Davidowitz has been able to unearth a lot of fascinating answers to puzzling questions. For example, he is able to predict, through Google searches for various symptoms, who is likely to have early stages of pancreatic cancer. He can predict epidemic breakouts of some contagious diseases well before they are announced by the CDC (Center for Disease Control). He shows that the single factor that correlates with voting for Trump is that of racism.Then there are the fun factoids, about the sorts of things that people search for most often on Google. Most commonly, the search "Is my son ..." is followed by "gifted", while the search "Is my daughter ..." is followed by "overweight". That tells us something about stereotypes for the way people think about their children. Interestingly, the release of a new violent movie in a city is correlated with a decrease in violent crime in that city. Perhaps the reason is that violent people who are watching the movie are not out on the streets, committing crimes.And here we get to the main problem with this sort of analysis. Undoubtedly, the research and analysis of big datasets is done correctly. However, once a surprising result is found, understanding the motivations behind the online activity are often subjective and open to interpretation. While this book is very careful about its underlying assumptions, it is a slippery road to getting the correct interpretations and explanations.This is an easy, well-paced book that should appeal to anybody who enjoys books like Freakonomics: A Rogue Economist Explores the Hidden Side of Everything.

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

I am now convinced that Google searches are the most important data set ever collected on the human psyche. writes the author early on & he shows why. (Google trends is available to all here: https://trends.google.com/trends/) He also checked other big data sets including Wikipedia, Facebook, Pornhub, & even Stormfront, the largest racist site. What he found was really interesting & it will help harden the soft, social sciences. It's a new frontier. He points out problems with traditional reporting. In the section about child abuse & abortions, Google searches suggest that child abuse does increase during economic downturns while gov't figures incorrectly show little change. Closing abortion clinics doesn't stop them, it simply leads to more self-induced abortions. Both happen off the books, but there is now convincing supporting data to show us what we need to address & make more informed decisions with resources.Big data has an advantage over every other type of survey because few realize it is being collected, so we don't lie to make ourselves look better. It's also anonymous & aggregate, so caution needs to be used when forming conclusions. For instance, based on Pornhub searches, the author concludes that about 5% of men are gay because they searched for gay porn. That seemed a reasonable conclusion until he pointed out that 15% of women search for rape porn. Does that mean they want to be raped? The author says of course not & makes a big deal out of the difference between fantasy & reality. That makes me question his first conclusion, although it seems about right.Gut reactions are often wrong & he provides several examples where it's wrong due to cognitive biases. He also points out "The Curse of Dimensionality". Given large enough sets of data, there will be correlations just through chance. For instance, there are graphs that show how closely autism diagnoses track with organic food sales or Jenny McCarthy's popularity. Separating these out is a whole other problem.Big Data only gives us trends that we need to examine. We can't use it on the individual level. While 1000 people searched for how to kill their girl friend, only 1 girl was killed in his example. That's horrific & might have been stopped if someone had looked at his search history, but do we give up everyone's privacy for a 1 in 1000 chance that we might prevent a murder? Some might be willing, but I'm not, so we also have new questions to address.The audio book was well narrated & I didn't miss the graphs too much. They're provided in the extra material, but weren't handy when I was listening & the book took that into account for the most part. Highly recommended in either format.

Acertei em cheio nessa leitura! Seth Stephens-Davidowitz apresenta uma análise de como as pessoas se comportam, na mesma linha do The Signal and the Noise: Why So Many Predictions Fail - But Some Don't e do Dataclisma: Quem somos quando achamos que ninguém está vendo. Mas enquanto Signal and the Noise fala de tendências de dados e Dataclisma fala do comportamento das pessoas dentro do OkCupid!, Everybody Lies fala de como as pessoas se comportam em geral.O autor usa uma série de dados de forma bastante inovadora, como tendências de buscas no Google (onde ele trabalha), buscas no PornHub, Facebook e outras fontes de big data para fazer o que ele chama de "sociologia de verdade" ou sociologia baseada em evidências. Os dados que ele mostra sobre preconceito (buscas por temas preconceituosos), insegurança de auto-imagem, inseguranças em relação aos filhos e afins mostram uma imagem bem mais crua e feia da sociedade do que o que pintamos com postagens em Facebook e Instagram. Outros revelam informações no mínimo interessantes, sobre a diferença que se formar em Harvard pode fazer (nenhuma, o ponto parece estar em quem se forma), onde criar os filhos, como aumentar as chances de sucesso em um encontro... O livro lembra bastante uma versão mais nova e, na minha opinião, mais curiosa da abordagem inovadora de Freakonomics.Se você não está interessado na revolução que o registro e a disponibilidade de dados está causando no mundo, e no estrago que empresas e governos conseguem fazer com o controle que têm sobre a informação, no mínimo vai curtir o livro pelos fatos curiosos e mórbidos que ele levanta dos dados. Saber por exemplo que o número de homens que buscam como fazer bem sexo oral nas mulheres é o mesmo que busca por como fazer sexo oral em si mesmo fala muito sobre como as pessoas pensam. Um livro para todos os gostos.

3.5 starsThis is an engaging and informative book about the huge amount of data available online and what it tells us about society. I read it alongside Dataclysm and found Everybody Lies to be by far the better of the two, presenting a wealth of information in a cohesive fashion and making fewer unfounded assumptions. The author was a data scientist at Google, and draws in large part on the searches people make on the site, along with information from sites including Facebook and Pornhub.There’s a lot of interesting stuff in the data, from the rate of racist searches in the rust belt predicting the rise of Donald Trump, to common body anxieties and whether they actually matter to the opposite sex, to an estimate of how many men are gay and whether that varies by geography (it appears not), to rates of self-induced abortions. This is a great book to read if you love unusual factoids, whether on sexual proclivities or how sports fans are made. The author also writes in a compelling way about the uses of Big Data itself, and while he waxes evangelical about it (evidently preferring to spend all his time immersed in statistically significant data, he finds novels and biographies too “small and unrepresentative" and therefore uninteresting), there are certainly a lot of possibilities there. In health, for instance, compiling early searches about symptoms with later searches for how to handle a diagnosis can help doctors detect pancreatic cancer at an earlier stage, while epidemics can be tracked through symptom searches. The author is also interested in how applying data can revolutionize a field, discussing at length the data that predicted the success of the racehorse American Pharaoh. (By "at length" I mean 9 pages; this is a book that moves through a broad range of topics quickly.)Overall, the writing is engaging and the book hangs together well, being informative while mostly resisting the urge to speculate. But the author does make a couple of assumptions worth pointing out. One is that people’s Google searches are made in earnest and for personal reasons. Certainly, you might search for “depression symptoms” out of concern that you or someone you know is depressed. But you also might want to be prepared in advance to identify warning signs, or might have encountered something in the media that sparked your interest, or you might be a student writing a paper on the topic. On the other hand, if you’re intimately familiar with depression already, you’re unlikely to google the symptoms. None of this means the author’s finding a 40% difference in rates of depression symptom searches between Chicago and Hawaii isn’t relevant, but data that’s both over- and under-inclusive serves better as a starting point for research than a definitive conclusion. It's certainly not proof that better geography is twice as effective as antidepressants, as the author suggests.The other assumption is that everybody lies: the book insists on it, based largely on the fact that typically rosy social media posts fail to reflect all those unhappy or hateful searches. Selectively sharing information doesn’t necessarily seem to me to be lying, but the author appears invested in proving the book’s title. For instance, he discusses a particular type of tax fraud: in areas where few tax professionals or people eligible for the scheme live, 2% of people who could benefit from this lie tell it, while in areas with high concentrations of both, the rate of cheating is around 30%. The author concludes that “the key isn’t determining who is honest and who is dishonest. It is determining who knows how to cheat and who doesn’t.” This bleak view of the world fails to account for the 70% who don’t cheat even in areas with high levels of knowledge; finding that significant numbers of people cheat if they know how is a far cry from finding that everyone does.So, like the author of Dataclysm, Stephens-Davidowitz is probably a better statistician than sociologist. But if you’re interested in Big Data, or in getting a peek at the thoughts and anxieties people ask Google about because they’re not comfortable sharing with others, this is the book I recommend. You’ll certainly get a lot of interesting tidbits from it, along with perhaps new inhibitions about typing things into Google!