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What's the Internet Hiding From You?

Filter bubbles help decide what you see in Google search results and on Facebook

You are probably familiar with the scenario of going to a store with a specific purpose in mind (such as buying linens) only to purchase another item (such as a bread maker) that you might never have considered buying if you hadn't seen it in the store.

Now consider this alternative scenario: Say in the last year you have bought a lawnmower, several books, and some shirts, along with that bread maker and a few other items. The next time you walk into your favorite store, these items and others like them are the only ones available to you. Certainly not the new keyboard that might have been your surprise purchase this time around. In other words, what is now available to you is only merchandise similar to the merchandise you have purchased before.

The concept of filter bubbles parallels this scenario, but instead of merchandise, the commodity being pre-sorted is information. In the case of your online activities, this is not a hypothetical situation; it's happening every time you use Google, Facebook, Yahoo! News, and many other websites.

These sites show you search results and feed items based on choices you have made in the past. For example, if you Like or comment on one friend's Facebook posts frequently, or look at their vacation photos, posts from this friend will be displayed to you more, while others will be displayed less. The "universe of information that you live in online" created by these filters is called a filter bubble.

Filter Bubbles: Good or Bad?
Public concern about filter bubbles has grown since the release in 2011 of the book, The Filter Bubble: What the Internet Is Hiding From You, by Eli Pariser, who uses the "universe of information" definition of filter bubbles and outlines his concerns about them in the TED talk viewable at

In this talk, Pariser points out that the search and feed results being shown to each individual are based on relevance to that individual. But, he says, "If algorithms are going to … decide what we get to see and what we don't get to see, then we need to make sure that … they also show us things that are uncomfortable, or challenging, or important … [or] other points of view." Some argue that algorithmic filters save time and effort in an online world filled with so much information that you could never sort through it on your own. For example, if you have no interest in gardening, you will not be shown books about gardening on Amazon, which is useful and positive. Another argument for filtering is that most people are capable of thinking beyond the information they are shown online.

Yet, Pariser points out that without seemingly irrelevant choices being given to you at least some of the time, you miss out on that experience of discovering a new idea or stumbling upon a happy surprise (like the bread maker in the store where you went to buy linens).

How to Burst Filter Bubbles
Those discussing filter bubbles online have differing views but generally agree on another one of Pariser's points: The rules by which filtering decisions are made should be more transparent. In his TED talk, Pariser addresses the folks behind Google, Facebook, and other online resources: "We need you to make sure that [these algorithms] are transparent enough that we can see what the rules are that determine what get through our filters. And we need you to give us some control …"

Whether those folks will respond to Pariser's request remains to be seen. In the meantime, there are many strategies you can use to "burst filter bubbles" on your own. Here are a few:

Whether you view filter bubbles as a timesaving convenience or a somewhat creepy intrusion, they're a fascinating topic that's certain to inspire more discussion in the years ahead.

Google's 57 Anonymous Signals

In Pariser's TED talk, he mentions that Google uses 57 anonymous signals about each user to determine which search results to show, even when the user is not logged into Google. A Google engineer revealed a few of them to Pariser: the computer you're using, your browser, and your location. The rest remain a mystery but the Filter Bubble website ( mentions a list created by Web science PhD student, Rene Pickhardt, that includes some guesses. Here are a few:

Source: google-uses-57-signals-to-filter/