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A filter bubble is the intellectual isolation that can occur when websites make use of algorithms to selectively assume the information a user would want to see, and then give information to the user according to this assumption. Websites make these assumptions based on the information related to the user, such as former click behavior, browsing history, search history and location. For that reason, the websites are more likely to present only information that will abide by the user's past activity. A filter bubble, therefore, can cause users to get significantly less contact with contradicting viewpoints, causing the user to become intellectually isolated.
Personalized search results from Google and personalized news stream from Facebook are two perfect examples of this phenomenon.
The term filter bubble was coined by internet activist Eli Pariser in his book, "The Filter Bubble: What the Internet Is Hiding from You" (2011).
Pariser relates a case in which a user searches for "BP" on Google and gets investment news regarding British Petroleum as the search result, while another user receives details on the Deepwater Horizon oil spill for the same keyword. These two search results are noticeably different, and could affect the searchers' impression of the news surrounding the British Petroleum company. According to Pariser, this bubble impact could have adverse effects for social discourse. However, others say the impact is negligible.