Construct · 1 fields · 3 citations
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Eli Pariser named the filter bubble in his 2011 book The Filter Bubble, describing the way algorithmic personalization on search engines and social platforms tailors what each user sees to their inferred preferences and past behavior, so that people are more often shown information that fits what they already believe and less often shown views they might disagree with. The term refers to a personal, largely invisible information environment: personal because it is built from an individual's own data, invisible because the user does not choose or see the filtering rules, and algorithmic because a ranking or recommendation system, not the user, decides what is included. Later large-scale studies tested how strong this narrowing actually is: Bakshy, Messing, and Adamic (2015) measured exposure to cross-cutting political content on Facebook and reported that users' own choices about what to click reduced ideological diversity more than the ranking algorithm did, while Flaxman, Goel, and Rao (2016) traced web-browsing histories and found that personalization was associated with both slightly more ideological segregation and greater exposure to opposing views. The construct is distinctive as a named account of how automated content selection, rather than human editorial judgment, shapes the range of information an individual encounters, and it is studied by comparing the ideological diversity of the content people are shown against the content they select.[1][2][3]
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