![]() ![]() Comparing two of the friends' first pages of results, while there was overlap between them on topics like news and travel, one friend's results prominently included links to information on the then-ongoing Egyptian revolution of 2011, while the other friend's first page of results did not include such links. In a test seeking to demonstrate the filter bubble effect, Pariser asked several friends to search for the word "Egypt" on Google and send him the results. Pariser's idea of the filter bubble was popularized after the TED talk in May 2011, in which he gave examples of how filter bubbles work and where they can be seen. Īs of 2011, one engineer had told Pariser that Google looked at 57 different pieces of data to personally tailor a user's search results, including non-cookie data such as the type of computer being used and the user's physical location. Īccessing the data of link clicks displayed through site traffic measurements determines that filter bubbles can be collective or individual. Open-even for an instant-a page listing signs that your spouse may be cheating and prepare to be haunted by DNA paternity-test ads. ![]() Share an article about cooking on ABC News, and you may be chased around the Web by ads for Teflon-coated pots. Search for a word like "depression" on, and the site installs up to 223 tracking cookies and beacons on your computer so that other Web sites can target you with antidepressants. Your identity shapes your media." Pariser also reports:Īccording to one Wall Street Journal study, the top fifty Internet sites, from CNN to Yahoo to MSN, install an average of 64 data-laden cookies and personal tracking beacons. Finally, you tune in to get the fit just right. Then, you provide them with content and services that best fit them. This process is not random, as it operates under a three-step process, per Pariser, who states, "First, you figure out who people are and what they like. An internet firm then uses this information to target advertising to the user, or make certain types of information appear more prominently in search results pages. Pariser defined his concept of a filter bubble in more formal terms as "that personal ecosystem of information that's been catered by these algorithms." An internet user's past browsing and search history is built up over time when they indicate interest in topics by "clicking links, viewing friends, putting movies in queue, reading news stories," and so forth. Social media, seeking to please users, can shunt information that they guess their users will like hearing, but inadvertently isolate users into their own filter bubbles, according to Pariser. presidential election in 2016 have been associated with the influence of social media platforms such as Twitter and Facebook, and as a result have called into question the effects of the "filter bubble" phenomenon on user exposure to fake news and echo chambers, spurring new interest in the term, with many concerned that the phenomenon may harm democracy and well-being by making the effects of misinformation worse. He related an example in which one user searched Google for "BP" and got investment news about British Petroleum, while another searcher got information about the Deepwater Horizon oil spill, noting that the two search results pages were "strikingly different" despite use of the same key words. Īccording to Pariser, users get less exposure to conflicting viewpoints and are isolated intellectually in their informational bubble. The bubble effect may have negative implications for civic discourse, according to Pariser, but contrasting views regard the effect as minimal and addressable. In Pariser's influential book under the same name, The Filter Bubble (2011), it was predicted that individualized personalization by algorithmic filtering would lead to intellectual isolation and social fragmentation. ![]() ![]() The term filter bubble was coined by internet activist Eli Pariser circa 2010. Prime examples include Google Personalized Search results and Facebook's personalized news-stream. The choices made by these algorithms are only sometimes transparent. Consequently, users become separated from information that disagrees with their viewpoints, effectively isolating them in their own cultural or ideological bubbles, resulting in a limited and customized view of the world. Personalized searches utilize website algorithms to selectively curate search results based on information about the user, such as their location, past click-behavior, and search history. The term filter bubble was coined by internet activist Eli Pariser, circa 2010.Ī filter bubble or ideological frame is a state of intellectual isolation that can result from personalized searches. ![]()
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