If you’ve enjoyed this review or have already read the book, please make sure to check back here next month! Our Measurement Life interview in February is with the author of Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are, Seth Stephens-Davidowitz.
The eye-catching title of this engrossing book is more than a way to capture readers’ attention: it is the fundamental narrative, in which Seth Stephens-Davidowitz delves into Google search data in a way that should interest any person who has ever typed a question into that open-form box.
In other words, this book should be of interest to the vast majority of us.
One thing to note at the outset is that Stephens-Davidowitz has an educational background that makes complete sense for this undertaking. Yes, he’s a data scientist. He’s worked at Google, and has a PhD in Economics from Harvard. However, his undergraduate degree from Stanford University is a BA in Philosophy, so he has the training in asking the big questions of life, and the research chops of a scientist when looking for the answers to those questions. This comes through clearly in the book, and makes it a joy to read.
Humans do lie, all the time. Especially when we’re creating and curating our lives for others—as we do on social media.
When are we honest? When we seek answers from an algorithm—when we’re alone, typing an anonymous question into a plain white box.
As Stephens-Davidowitz notes “…people’s search for information is, in itself, information.”
Why search data?
Anyone interested in measurement and analysis should read this book, as the information uncovered by examining search data is compelling. I could fill this review with examples, but the one that sticks out in my mind the most clearly is in Chapter 4, titled “Digital Truth Serum.” During the Great Recession, child advocates were concerned that the financial stress placed on families could cause an uptick in child abuse and neglect cases. When the reporting data started to come in, it reflected just the opposite situation: reporting of child abuse and neglect cases dropped overall, and saw the largest declines in states that were hardest hit by the recession.
This seemed remarkably unlikely to reflect reality, so Stephens-Davidowitz looked at Google search data, where he found that the number of heart-wrenching Google searches carrying phrases like “my mom beat me” or “my dad hits me” went up, tracked the unemployment rate, and increased in areas hardest hit by the downturn. In other words, reporting child abuse went down during the recession—not the child abuse itself. Other data backed this up, sadly and most notably the number of child deaths caused by abuse and neglect.
Anonymous searches can provide a window into behavior that typically remains hidden.
This is valuable information to have because it can point us to where we should find solutions. In fact, shortly after I read the book the New York Times Magazine published an article about how algorithms are being deployed to detect child abuse.
When we have the right data, and enough of it, we can solve problems.
Surveys and social media—and why we need to be cautious about basing decisions on this data
Communicators and PR professionals should read it because of the accessible manner in which Big Data—its pitfalls and its promises—is depicted.
With communicators investing heavily in research tools such as surveys—and by investment, I mean not just money and time, but also using these tools to measure their own performance and the success of their initiatives—we are trusting that results will be accurate enough to base business decisions on their results.
Seeing the stark differences between survey reporting with actual results means that we must factor in a certain amount of error. But what if search data could give us a clearer picture at the outset? In bypassing the tendency of people to lie on surveys, we could potentially get a better, more accurate result IF we know where to look, and what questions to ask.
When we’re reviewing content from social media to draw insights for clients, it’s a good idea to think through what we’re hoping to learn. If you’re hoping to draw conclusions about how people really feel about their spouses and significant others by monitoring Facebook, you might want to reconsider. The disparity between how people talk about their partners on social media versus the most common Google searches was an eye-opener.
This doesn’t mean the information is useless—just proceed with caution.
More data does not magically mean greater insight
Businesses, he notes, are “drowning in data.” We are collecting massive amounts of information and storing it—and this is one of the notable things that has changed over the years. Not only are our connected and online lives generating far more data than we have before, the cost to store this information has become dramatically cheaper. We also need less physical space to store this information.
It is what we do with information that makes it valuable, which is something we spend a fair amount of time discussing when we are talking about measuring what matters. Collecting media coverage doesn’t provide us with insight unless it is analyzed. Having six years’ worth of sentiment on client mentions doesn’t do us any good if it’s never looked at. This could well be the most important message in the book for communicators and PR pros to understand.
Software allows us to collect and review more and more data every day. It’s easy to get overwhelmed or put things on autopilot and never revisit what you are collecting, why you are collecting it, and what you can do with the information. Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are will start you thinking about data in a different way—which is critical for communicators to understand as PR changes.
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