Method 1: Large-Scale Aggregation

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There are a lot of ways that companies gather your data. One method, which I find a tiny bit creepy, is large-scale aggregation.

When you perform a Google search for “cold symptoms,” you probably think that (1) your search is private, (2) no one really cares about what you Google anyway, and (3) you’ll get unbiased information in return.

Unfortunately, none of this is true.

First, as personal as your searches are — particularly ones about health, finances, or relationships — they’re never private. If you’re logged into your account, Google will associate all browsing activity to you, prioritizing your common searches for later.9 Even if you’re not logged in, your browser itself will save your searches in its History section.10 Finally, even if you go to great lengths to privatize your search — logging out of your account, clearing your history, and/or using your browser’s Incognito mode — your search still isn’t private.

This is because Google tracks everything you do on its platform, even if they can’t associate it with you specifically. Your metadata still says a lot, allowing Google to place you into some rough categories.11 For instance, if you Google “cold symptoms” today, your metadata might show that you’re an English-speaker on an iPhone in New Carolina.

This doesn’t seem very powerful on its own; who cares if an English-speaking iPhone user in NC is thinking about “cold symptoms”?

Well, when this data is combined with the data of millions of other people, it becomes quite powerful. If a few million North Carolinians Googled “cold symptoms” this week, Google could deduce that a cold was breaking out in the area. Or if mobile users were Googling “cold symptoms” at a higher rate than desktop users, Google could determine that the mobile users presented a better marketing opportunity for cold medications.12

In essence, Google aggregates data on a large scale to identify trends across its user population, which gives them insight into what’s happening in the world, both on- and offline.

By the way, the cold-related example I’ve given didn’t come out of thin air. Google actually launched a program called Google Flu Trends in 2008.13 Although they no longer share their findings publicly, you can peruse data from previous years here and you can compare it across locations here. For instance, below is a quick comparison I did of North Carolina ‘s flu search activity to California’s over the course of 11 years.

So, now we know that (1) your Google activity is not private and (2) other people do care about what you search. But what about the idea that (3) you get shown unbiased information?

Given how much Google knows about you — both individually and on a large-scale — it has a huge opportunity to tailor what you see in return for your searches, particularly in terms of ads. Google can even sell aggregated data to other interested parties, as they did with Blue Chip Marketing and Vicks.

In 2011, Vicks (a brand of over-the-counter medications owned by Procter & Gamble) hired Blue Chip Marketing Worldwide to target ads for its Behind Ear Thermometer. Blue Chip decided to use Google Flu Trend data to locate “states that had high ‘flu’ activity. It then targeted users in those states on the basis of their ‘gender,’ ‘age,’ and ‘marital status’ and if they were a ‘parent.’ [The author put these categories are inside quotation marks because they’re presumed by Google based on metadata.] Finally, Blue Chip would wait until those profiled users’ GPS locations were within three miles of a retail store that sold the Behind Ear Thermometer and then trigger a targeted advertisement on those users’ phones.”14

So, no, you don’t get unbiased information in return for your Google searches. Thanks to large-scale aggregation, you get ads depending on where you are, who you are, who bought your data, and how they hope to turn a profit.

Keep in mind, Google Flu Trends is just one example of how Google uses large-scale aggregation; there are many additional ways they can profit from it.15 And other corporations with large user bases, like Amazon, Netflix, and Facebook, take advantage of it too.16

All in all, I find this method of gathering data to be only “a tiny bit creepy” because we’re being exploited for our data (yikes), but at least we’re in a herd. Any given individual as some semblance of anonymity.

I wish I could say the same for these other methods of gathering data:

Method 2: Web Analytics
Method 3: Terms & Conditions
Method 4: Third-Party Cookies
Method 5: Internet Service Providers

Header image by Gregor Cresnar from Noun Project.

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References:

  1. Delete search history & other data.” Google. Web. Accessed on 11/26/17.
  2. Clear browsing data.” Google. Web. Accessed on 11/26/17.
  3. John Cheney-Lippold, We Are Data: Algorithms and The Making of Our Digital Selves. New York University Press. 2017 (6)
  4. John Cheney-Lippold, We Are Data: Algorithms and The Making of Our Digital Selves. New York University Press. 2017 (123).
  5. The Next Chapter for Flu Trends.Google. August 20, 2015. Web. Accessed on 11/26/17.
  6. John Cheney-Lippold, We Are Data: Algorithms and The Making of Our Digital Selves. New York University Press. 2017 (123).
  7. Google Trends.” Google. Web. Accessed on 11/26/17.
  8. AWS Big Data Solution Showcase.” Amazon. Web. Accessed on 11/26/17.
  9. Delete search history & other data.” Google. Web. Accessed on 11/26/17.
  10. Clear browsing data.” Google. Web. Accessed on 11/26/17.
  11. John Cheney-Lippold, We Are Data: Algorithms and The Making of Our Digital Selves. New York University Press. 2017 (6)
  12. John Cheney-Lippold, We Are Data: Algorithms and The Making of Our Digital Selves. New York University Press. 2017 (123).
  13. The Next Chapter for Flu Trends.Google. August 20, 2015. Web. Accessed on 11/26/17.
  14. John Cheney-Lippold, We Are Data: Algorithms and The Making of Our Digital Selves. New York University Press. 2017 (123).
  15. Google Trends.” Google. Web. Accessed on 11/26/17.
  16. AWS Big Data Solution Showcase.” Amazon. Web. Accessed on 11/26/17.
  17. Delete search history & other data.” Google. Web. Accessed on 11/26/17.
  18. Clear browsing data.” Google. Web. Accessed on 11/26/17.
  19. John Cheney-Lippold, We Are Data: Algorithms and The Making of Our Digital Selves. New York University Press. 2017 (6)
  20. John Cheney-Lippold, We Are Data: Algorithms and The Making of Our Digital Selves. New York University Press. 2017 (123).
  21. The Next Chapter for Flu Trends.Google. August 20, 2015. Web. Accessed on 11/26/17.
  22. John Cheney-Lippold, We Are Data: Algorithms and The Making of Our Digital Selves. New York University Press. 2017 (123).
  23. Google Trends.” Google. Web. Accessed on 11/26/17.
  24. AWS Big Data Solution Showcase.” Amazon. Web. Accessed on 11/26/17.