Isn't it rather suspicious when you're shopping for standard white notepads that you run into thousands of reviews of them on Amazon?
This is what I'm talking about:
This popular item on Amazon has amassed over 1,000 reviews. What could these people be saying? What could be worth saying about stacks of blank white paper with lines on them? A low-cost, disposable item that will be thrown away during consumption? When you learn that there are already 200 essays about this one notepad, do you feel a need to add to the pile? Is there anything original left to be said?
The natural question is how many of these reviews are fake - paid for by sellers? In my latest video, I talk about how fake reviews are made on Amazon, including why reviews labeled as "verified purchases" are just as fake.
I've talked about "fake data" before. A lot of fake data come from people trying to game algorithms or skew metrics, and oftentimes, automated bots are involved. Attempts to obscure these tactics typically involve creating layers of complexity so it is not easy to connect the dots.
I come across suspicious data all the time, and it's not always clear what's going on or why. So I thought I'd feature some of them here and see if anyone can figure it out.
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This account on Yelp caught my attention because this user apparently uploaded a photo of her desktop to one of the restaurant pages. She has uploaded a total of four photos, all of them are unrelated to food. The four photos were uploaded to two New York City restaurants while she indicated that she lives in San Francisco. She did not review either of those NYC restaurants but she has written one review for a cafe in Long Island City. The review seems genuine (although it's hard to tell unless you've been to that cafe).
She has five friends. While she lives in San Francisco, these friends live in Manhattan, Brooklyn, Scottsdale and Oceanside. She has no friends in the Bay Area. None of these friends have ever written a single review and have no likes. However, each of these friends have 100-400 friends. It's not clear why one would be friends with someone on Yelp who has no reviews or likes.
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Is this account fake? If so, why was it created? How did those photos get uploaded? How did they get placed in those particular restaurants? Who are these friends? Are they fake as well? If the account is fake, was that review also fake? Is it possible to predict that the review is fake?
So many questions, and so hard to get answers. What do you think is going on?
In the latest video, I introduce the unsung heroes behind the AI tech that drives those popular smart home assistants, like Alexa and Google Home. These are the minimum-wage workers in India, China, and other places with cheap labor who are tasked with listening to the recordings from your home conversations.
This practice is raising a lot of concern and controversy. Amazon, Apple and Google have all announced that they are suspending the listening programs. Nevertheless, this hiatus is likely temporary because the machine learning methods behind such tech are extremely data-greedy, and must be trained with human input.
Just recently, I made a short clip about how Grubhub is extracting lead-generation fees from unsuspecting restaurant owners by setting up a network of shadow websites (and phone numbers). Diners who thought they were ordering directly from restaurants were instead shuffled through the Grubhub toll booth. Click here to see how this works.
Now, Vice discovered yet another of Grubhub's toll booths. This time, the toll booth is set up on the Yelp superhighway. When a Yelp user clicks on the restaurant's phone number on its Yelp page, a pop-up shows up, with two options, one of which redirects the user to a shadow number owned by Grubhub. These numbers are not labelled "direct" and "Grubhub" but "general questions" and "deliveries and orders". So now both Yelp and Grubhub are making lead-generation money off the restaurant owner.
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This dispute is about causality! And because causality is tough to establish, it creates a gray zone of disagreement.
Ideally, the restaurant owner pays for orders caused by Grubhub's marketing activities. By cause, we mean the orders would not have materialized without Grubhub's marketing. In the examples we saw, it's highly unlikely that Grubhub did anything to cause those orders. That's because those diners thought they were ordering directly from the restaurants - they are quietly re-routed to the shadow websites and phone numbers set up by Grubhub, sometimes without even the restaurants' knowledge.
It is the secrecy that gives the game away. If Grubhub's causal value is clear, it can form open partnerships with the restaurants without resorting to trickery.
As I pointed out in the video, the majority of the digital marketing industry relies on similar tactics. Search engines do not typically send you directly to the webpage you clicked on - you are often rerouted through the search engine's server, so that the search engine can "track" the click and use the paper trail to receive lead-generation money. Anyone passing through this toll booth is counted as "causal" but in reality, many of these users would have found their way to the webpage even if the search engine didn't exist. (Consider going directly to Macys.com instead of typing Macys into a search engine.)
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This is a great example of how data, algorithms and software are silently running our lives, and often to the detriment of those who don't understand what's going on. Our video series is a small effort to help you stay in front of these data-driven technologies.
The more you know, the more you can leverage its power, and avoid its harms.
Kaiser Fung. Business analytics and data visualization expert. Author and Speaker. Visit my website. Follow my Twitter. See my articles at Daily Beast, 538, HBR.
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