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What is a Filter Bubble?

A filter bubble is our personal space that we live in online. It is created by algorithms that select content for us based upon past activity and who we are. The result is that we see content that supports our existing biases and perspectives and are not exposed to opposing viewpoints.

Beware of Online "Filter Bubbles"

Confirmation Bias


One way to see how filter bubbles work with search engines that do personalization (like Google) is to take a word that can have multiple meanings in different contexts and build up different search histories using those contexts. Then, when you search for the same word after having built up different search histories, the search engine should return results that look a bit different.

For this demonstration to work, you need to be sure to clear your search history before you start each round. This works even better if you have 2 or 3 people working side by side at different computers. That way you can compare the results more easily.

Try this with the word Tea.

1. Have someone build a search history using names of countries where tea is popular or names of countries where teas orgininated. Remember, do not use the word "tea" as a search term quite yet. Examples would be England, Japan, China, Latin America, etc.

2. Have another person build a search history using different spices, herbs, and flowers that make up common teas. Examples would be roses, cinammon, chrysanthemum, lavender, etc.

3. Have a third person search for anything related to politics, such as names of political parties (not the Tea Party just yet, though!), names of political movements, words like "activism," or "conservative" and "liberal."

4. When you are performing these searches, click on some of the results (preferably general ones that might somehow later be connected to tea!). This will contribute to your search history.

5. Finally, have everyone search for the word "Tea." Have fun comparing results!

Credit:  Adapted from University of Illinois Urbana-Champaign Library, Ways of the Web: Filter Bubbles and the Deep Web, 2017