“Filter bubble” is a term by Internet activist Eli Pariser associated with searching the World Wide Web. A filter bubble is a result state in which a website algorithm selectively guesses what information a user would like to see based on information about the user (such as location, past click behavior and search history) and, as a result, users become separated from information that disagrees with their viewpoints, effectively isolating them in their own cultural or ideological bubbles. According to Pariser, users get less exposure to conflicting viewpoints and are isolated intellectually in their own informational bubble. A filter bubble means that a user will get search results and other web related information based on a user’s avatar (or personality). The bubble effect may have negative implications for civic discourse, according to Pariser, but there are contrasting views suggesting the effect is minimal and addressable. Filter bubbles go hand-in-hand with Web 3.0.
The Era of Personalization
Many would argue that a filter bubble is a positive thing; producing specific results catered to a user. As of December 2009, search engines no longer produce generic search results. They are specific to a user based on traits such as prior searches, geographic location, and even a user’s Facebook friend’s habits. Computers use algorithms to determine how relevant a web page is to a specific user’s search. Relevancy to many different critera is taken into consideration when ranking web sites from the index for a user.
Eli Pariser related an example in which two similar users were asked to perform a search for “BP” during the time of a massive oil spill. One user yielded results focusing on company investment while the other searcher got information about the Deepwater Horizon oil spill. The two search results pages were very different. They were each put in a “filter bubble” that had been designed by data tracking and avatars. Other Prime examples are Google 's personalized search results and Facebook's personalized news stream. RSS feeds and sites such as BuzzWorthy focus their results on filter bubbles.
Examples of how sites that take advantage of filter bubbles:
- Facebook (new friend suggestions, “news”)
- Netflix (movie suggestions)
- Yelp (restaurant suggestions)
- OkCupid (mate suggestions)
As the World Wide Web approaches Web 3.0, the hope is to link multiple pages of information. For example, a dating site such as OkCupid would make a suggestion for a mate but filter bubbles would also take that a step further and suggest a restaurant and movie as well. The data tracking raises privacy concerns for some users.
As applications such as RSS feeds and other Web 3.0 technologies emerge, there are also many concerns over filter bubbles. With over 60 trillion web pages, it is possible to be completely cut-off from certain information unless a specific search is performed. This means that a major disaster could be happening and it may not even be in the top-ranked search results for a user. Assumptions may be made about income and more or less expensive items may be advertised.